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position: absolute; bottom: 0; width: 95%; margin-bottom: 10px; } .pubs .row { position: relative; } .pubs .col-sm-9 { margin-bottom: 400px; } .standards .ISTLeftNav_BigButtonSingle { float: right; position: absolute; bottom: 0; width: 95%; margin-bottom: 10px; } .standards .row { position: relative; } .standards .col-sm-9 { margin-bottom: 400px; } .JournPI .ISTLeftNav_BigButtonSingle { float: right; position: absolute; bottom: 0; width: 88%; margin-bottom: 10px; } .JournPI .row { position: relative; } .JournPI .col-sm-9 { margin-bottom: 700px; } } @media (max-width: 767px) { .footer-col { float: left; width: 100%; text-align: left; } .footer-col ul { padding-bottom: 10px; margin-left: 5px; } .footer-col li { padding-top: 0px; } .footer-content { background-color: #fff; color: #004a80; text-align: center; border-top: #004a80 1px solid; padding-top: 40px; float: left; position: absolute; width: 100%; } .footer-nav { float: left; position: relative; width: 100%; } .FooterTop { float: left; width: 100%; margin: 30px 0 20px 0; } .FooterLogo { margin-left: 0px; text-align: left; float: left; } .FooterLogo img { margin-right: 0px; width: 100px; } .FooterSocial { padding: 0; height: 95px; } .FooterSocial p { margin: 13px 0px 0px 40px; float: right; } .FooterSocialText { float: right; width: 60%; text-align: right; } .FooterSocialImg { float: right; width: 60%; text-align: right; } .FooterCopyright { padding: 0px; text-align: left; } .FooterPolicies { float: left; } } </style><link href="/WebResource.axd?d=vsKpXOsoEwDUfVug9KOqSkSpSXVaNBxqYq0TMVVWWh_D58LwxadHoTawtKs_sAzbrfD81FIKiaLMHfE8C-_1biM4aI5bWvBCogQPX0gqgkMOYJs3nRZiavvQhAxxuDxC0&t=638584690780000000" type="text/css" rel="stylesheet" class="Telerik_stylesheet" /><link href="/WebResource.axd?d=HkmiNSkKJcp9zYO7iQ4LQYfcigDVRny_bpX66yTnidFPn8M0fokYr_cMbxH1xEMngudkfC-ZQGay1WGMDdRErxNZUgamrg-I8VpzCrtaoIMj66mt5nON1ewFVLY5L_q3Yce4_RXJFjxRuseRI8WsKg2&t=638584690780000000" type="text/css" rel="stylesheet" class="Telerik_stylesheet" /><link href="/WebResource.axd?d=501uTrfxke03AuVmqnFlcktdinHr9iVATciqFqhrqcOKTkomCd2QsHcrpjM_-xP-CqLQMjvRfrLOklibzCo45kW9flZoyUMNvcu5Mzw18RmO3Ap9KCdkr0PDfDKg4UD2xehjr4iqZqrdKJTFAyVnpJ_TSCRLyoWqgAqgqCSum1w1&t=638584690780000000" type="text/css" rel="stylesheet" class="Telerik_stylesheet" /><link href="/WebResource.axd?d=lsHxUYuoKn-ifTgGVgyNZQIZvqADQrnISEv67X7zPJZKRrk0V0-vW7xMMI_xGUYrNRgY0V24wprRwBJ4JAXB-PL_gpUMjcMBvixi6LjgrUFLgqzRtutTUA_SoLdglMWBbO_rKbCsgkpfQKzxCPGYpg2&t=638584690780000000" type="text/css" rel="stylesheet" class="Telerik_stylesheet" /><link href="/WebResource.axd?d=QKX1AO1vX8ebWKfbb4eOTEvhmgWml__oci93TQX-3srWTTq0hFmFYaJFDB9UKx-GfZqFRMKXn0jqsLJL5ywha6wcGEkFGP5adK9HNSwDXjbr9acE3_Dn_vJGCZ6A4DPI0&t=638584690780000000" type="text/css" rel="stylesheet" class="Telerik_stylesheet" /><link href="/WebResource.axd?d=_s8C6V0hVmZ50IR7zUlbBIx5hUAxbrT46F-Qe8ZhKIJt5wIqEePTdOgtTXhtJoRp9My5-D7o68asFZMfXdfLr664FIRyOvnOI7-P_C5gb1tC239yby2f-BLyA5vIZfs93zIzosPTIzGVgEB2TKXRYc6QuLCwX1Z_PNYRI9JRlaA1&t=638584690780000000" type="text/css" rel="stylesheet" class="Telerik_stylesheet" /><meta property="og:title" content="Media Watermarking, Security, and Forensics 2023 (MWSF)" /><style type="text/css"> .ctl01_TemplateBody_ContentPage1_WebPartZone1_Page1_0 { border-color:Black;border-width:1px;border-style:Solid; 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width: 400px;"><img src="/images/IST_website_banner.png" style="width: 434px; height: 93px;"></div> </a></strong></div><div class="ContentRecordPageButtonPanel"> </div> </div> </div> </div> <div class="navbar-header nav-auxiliary pull-right" id="auxiliary-container" data-label="Utility"> <div ID="WTZone2_Page1" class="WTZone "> <div id="ste_container_ciUtilityNavigation" class="ContentItemContainer"><div id="ctl01_ciUtilityNavigation_UtilityPlaceholder" class="UtilityNavigation nav-auxiliary" role="navigation"> <div class="UtilitySection UtilityAccountArea"> <ul id="ctl01_ciUtilityNavigation_ctl01_AccountMenu" class="account-menu"> <li id="ctl01_ciUtilityNavigation_ctl01_SignInLI"> <a id="ctl01_ciUtilityNavigation_ctl01_LoginStatus1" class="sign-in-link" translate="yes" href="javascript:__doPostBack('ctl01$ciUtilityNavigation$ctl01$LoginStatus1$ctl02','')">Sign in</a> </li> </ul> <script> function ToggleOBO(toggle_id) { var $this = jQuery("#" + toggle_id); var enabled = $this.hasClass("on"); var button; if (enabled) // click the clear button button = window.$get('ctl01_ciUtilityNavigation_ctl01_OBOControlPanel_ClearContactButton'); else // click the select button button = window.$get('ctl01_ciUtilityNavigation_ctl01_OBOControlPanel_SelectContactButton'); if (button != null) button.click(); } jQuery(document).ready(function () { jQuery('.website-item:gt(2)').hide(); jQuery('.js-show-more-sites').show(); jQuery('.js-show-more-sites a').on("click", function () { jQuery('.website-item:not(:visible):lt(5)').fadeIn(function () { if (jQuery('.website-item:not(:visible)').length == 0) { jQuery('.website-item a').last().focus(); jQuery('.js-show-more-sites').remove(); } }); return false; }); }); </script> </div><div class="UtilitySection UtilityNavigationList"> <ul class='NavigationUnorderedList'><li id="ctl01_ciUtilityNavigation_ctl03__rptWrapper__rptWrapper_rpt_ctl01_NavigationListItem" class="NavigationListItem nav-aux-button nav-aux-cart"><a id="ctl01_ciUtilityNavigation_ctl03__rptWrapper__rptWrapper_rpt_ctl01_NavigationLink" class="NavigationLink" onClick="MenuLI_OnClick('/IST/IST/iMIS/Store/StoreLayouts/Cart_OLD.aspx?hkey=f596492d-3628-4f58-b5ac-bbf9ff3a0784')" href="/IST/IST/iMIS/Store/StoreLayouts/Cart_OLD.aspx?hkey=f596492d-3628-4f58-b5ac-bbf9ff3a0784"><span class="nav-text" translate="yes">Cart </span><span><span data-cartlink='y' class="cartEmpty"><span class="cartSprite"></span></span></span></a></li><li id="ctl01_ciUtilityNavigation_ctl03__rptWrapper__rptWrapper_rpt_ctl02_NavigationListItem" class="NavigationListItem"><a id="ctl01_ciUtilityNavigation_ctl03__rptWrapper__rptWrapper_rpt_ctl02_NavigationLink" class="NavigationLink" onClick="MenuLI_OnClick('/IST/IST/MyAccount/CreateAccount.aspx?hkey=44e40f35-cbf4-4ac4-929b-39a8d9f1668b')" href="/IST/IST/MyAccount/CreateAccount.aspx?hkey=44e40f35-cbf4-4ac4-929b-39a8d9f1668b"><span class="nav-text" translate="yes">Create Account</span></a></li></ul> </div><div class="UtilitySection UtilityNavigationToggle"> <button onclick="return false;" class="navbar-toggle collapsed menu-toggle" data-toggle="collapse" data-target=".navbar-collapse" data-parent=".navbar"><span class="sr-only" translate="yes">Toggle navigation</span> <span class="icon-bar"></span> <span class="icon-bar"></span> <span class="icon-bar"></span> </button> </div><div class="UtilitySection UtilitySTEToggle ste-section"> </div> </div> <script type="text/javascript"> //<![CDATA[ // adapted from bs.collapse - if the search bar is open when opening the menu, close it (and vise versa) function UtilityAreaAction(utilityControl, hideArea) { jQuery(utilityControl).on('show.bs.collapse', function() { var actives = jQuery(hideArea).find('> .in'); if (actives && actives.length) { var hasData = actives.data('bs.collapse'); if (hasData && hasData.transitioning) return; actives.collapse('hide'); hasData || actives.data('bs.collapse', null); } }); } jQuery(document).ready(function () { UtilityAreaAction('.navbar-collapse', '.nav-auxiliary'); UtilityAreaAction('.searchbar-collapse', '.primary-navigation-area'); }); //]]> </script> </div> <div id="ste_container_ciNewUtilityNavigationCommon2" class="ContentItemContainer"><div id="ctl01_ciNewUtilityNavigationCommon2_UtilityPlaceholder" class="UtilityNavigation nav-auxiliary"> <div class="UtilitySection UtilityNavigationToggle"> <button onclick="return false;" class="navbar-toggle collapsed searchbar-toggle" data-toggle="collapse" data-target=".searchbar-collapse" data-parent=".navbar"><span class="sr-only" translate="yes">Toggle search</span> <span class="icon-search"></span> </button> </div><div class="UtilitySection UtilitySearch"> <div class="search-container-sm" data-set="searchbar"> <div class="header-search"> <div id="SimpleSearchBlock" role="search" class="SimpleSearchBlock"><label for="ctl01_ciNewUtilityNavigationCommon2_ctl05_SearchTerms" id="ctl01_ciNewUtilityNavigationCommon2_ctl05_Prompt" class="screen-reader-text" translate="yes">Keyword search</label><input name="ctl01$ciNewUtilityNavigationCommon2$ctl05$SearchTerms" type="text" value="Keyword search" id="ctl01_ciNewUtilityNavigationCommon2_ctl05_SearchTerms" class="search-field Watermarked" onfocus="SimpleSearchField_OnFocus('ctl01_ciNewUtilityNavigationCommon2_ctl05_SearchTerms', ctl01_ciNewUtilityNavigationCommon2_ctl05_SearchTermsProperties);" onblur="SimpleSearchField_OnBlur('ctl01_ciNewUtilityNavigationCommon2_ctl05_SearchTerms', ctl01_ciNewUtilityNavigationCommon2_ctl05_SearchTermsProperties);" onkeypress="return clickButton(event,'ctl01_ciNewUtilityNavigationCommon2_ctl05_GoSearch');" translate="yes" /><input type="button" name="ctl01$ciNewUtilityNavigationCommon2$ctl05$GoSearch" value="Go" onclick="SimpleSearchField_ExecuteSearch('ctl01_ciNewUtilityNavigationCommon2_ctl05_SearchTerms', ctl01_ciNewUtilityNavigationCommon2_ctl05_SearchTermsProperties); return cancelEvent();__doPostBack('ctl01$ciNewUtilityNavigationCommon2$ctl05$GoSearch','')" id="ctl01_ciNewUtilityNavigationCommon2_ctl05_GoSearch" class="TextButton" /></div> </div> </div> </div><div id="ctl01_ciNewUtilityNavigationCommon2_search-collapse" class="searchbar-collapse collapse" style="height:auto;"> <div class="search-container" data-set="searchbar"> <div class="header-search"> <div id="ResponsiveSimpleSearchBlock" role="search" class="SimpleSearchBlock"><label for="ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTerms" id="ctl01_ciNewUtilityNavigationCommon2_ctl08_Prompt" class="screen-reader-text" translate="yes">Keyword search</label><input name="ctl01$ciNewUtilityNavigationCommon2$ctl08$SearchTerms" type="text" value="Keyword search" id="ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTerms" class="search-field Watermarked" onfocus="SimpleSearchField_OnFocus('ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTerms', ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTermsProperties);" onblur="SimpleSearchField_OnBlur('ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTerms', ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTermsProperties);" onkeypress="return clickButton(event,'ctl01_ciNewUtilityNavigationCommon2_ctl08_GoSearch');" translate="yes" /><input type="button" name="ctl01$ciNewUtilityNavigationCommon2$ctl08$GoSearch" value="Go" onclick="SimpleSearchField_ExecuteSearch('ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTerms', ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTermsProperties); return cancelEvent();__doPostBack('ctl01$ciNewUtilityNavigationCommon2$ctl08$GoSearch','')" id="ctl01_ciNewUtilityNavigationCommon2_ctl08_GoSearch" class="TextButton" /></div> </div> </div> </div> </div> <script type="text/javascript"> //<![CDATA[ // adapted from bs.collapse - if the search bar is open when opening the menu, close it (and vise versa) function UtilityAreaAction(utilityControl, hideArea) { jQuery(utilityControl).on('show.bs.collapse', function() { var actives = jQuery(hideArea).find('> .in'); if (actives && actives.length) { var hasData = actives.data('bs.collapse'); if (hasData && hasData.transitioning) return; actives.collapse('hide'); hasData || actives.data('bs.collapse', null); } }); } jQuery(document).ready(function () { UtilityAreaAction('.navbar-collapse', '.nav-auxiliary'); UtilityAreaAction('.searchbar-collapse', '.primary-navigation-area'); }); //]]> </script> </div> <div id="ste_container_ciNewContentHtml_4c5b80c800b04a9a932a995d85dcc730" class="ContentItemContainer"><div class="HeaderSocial"><div id="ctl01_ciNewContentHtml_4c5b80c800b04a9a932a995d85dcc730_Panel_NewContentHtml"> <a href="https://www.linkedin.com/company/society-for-imaging-science-and-technology-is&t-"><img src="/images/Icons/linkedin36blue.png" alt="" style="width: 15px; height: 13px; margin-right: 10px;"></a> </div></div></div> </div> </div> </div> </div> <div data-label="Primary" class="ClearFix header-bottom-container"> <div class="header-container"> <div ID="WTZone3_Page1" class="WTZone "> <div id="ste_container_ciPrimaryNavigation" class="ContentItemContainer"> <div id="ctl01_ciPrimaryNavigation_PrimaryNavigationArea" class="primary-navigation-area"> <div id="ctl01_ciPrimaryNavigation_PrimaryNavigationControl" class="collapse navbar-collapse nav-primary"> <nav id="ctl01_ciPrimaryNavigation_NavControl_NavWrapper" aria-label="primary"> <div tabindex="0" id="ctl01_ciPrimaryNavigation_NavControl_NavMenu" class="RadMenu RadMenu_NaturalHeritageSites MainMenu MegaDropDownMenuOuter" Translate="Yes" PerspectiveId="80409b89-ae6d-45a9-a9d4-96d522ff2047" NavigationArea="1" MaxDataBindDepth="3" style="z-index:2999;"> <input class="rmRootGroup rmToggleHandles rmHorizontal" id="ctl01_ciPrimaryNavigation_NavControl_NavMenu_ClientState" name="ctl01_ciPrimaryNavigation_NavControl_NavMenu_ClientState" type="hidden" /> </div> </nav> </div> </div></div> </div> </div> </div> <div id="HomePageContent" class="HomePageFullWidthArea" data-label="Home Content"> <div ID="WTZone4_Page1" class="WTZone "> <div id="ste_container_ciFullWidthContent" class="ContentItemContainer"> </div> </div> </div> <div class="InternalHeaderArea" data-label="Internal Header"> <div ID="WTZone5_Page1" class="WTZone "> <div id="ste_container_ciInternalFullWidthHeader" class="ContentItemContainer"> </div> </div> </div> </header> <div id="masterContentArea" class="container ClearFix"> <div role="main" class="body-container" id="ContentPanel"> <div data-label="On Behalf Of" class="ClearFix OnBehalfOfContainer"> <div ID="WTZone6_Page1" class="WTZone "> <div id="ste_container_ciOBO" class="ContentItemContainer"><div class="FloatRight"><div id="ctl01_ciOBO_UtilityPlaceholder" class="UtilityNavigation nav-auxiliary"> <div class="UtilitySection OnBehalfOf"> </div> </div> </div></div> </div> </div> <div class="col-primary" data-label="Main Content"> <div ID="WTZone7_Page1" class="WTZone "> <div id="mainContentWrapper" class="ContentPanel"> <div id="masterMainBreadcrumb" data-height-offset="true" ></div> <div id="ctl01_UserMessagesUpdatePanel"> </div> <script type="text/javascript"> </script> <div id="ctl01_TemplateBody_PublishUpdatePanel"> </div><div id="ctl01_TemplateBody_WebPartManager1___Drag" style="display:none;position:absolute;z-index:32000;filter:alpha(opacity=75);"> </div> <div> <div class="row"> <div class="col-sm-3"> <div class="ContentItemContainer"> <div id="WebPartZone1_Page1" class="WebPartZone "> <div class="iMIS-WebPart"> <div id="ste_container_ciCornerArt" class="ContentItemContainer"><div id="ctl01_TemplateBody_WebPartManager1_gwpciCornerArt_ciCornerArt_ListTitle" class="panel " style="border-style:None;"> <div id="ctl01_TemplateBody_WebPartManager1_gwpciCornerArt_ciCornerArt_ListTitle_Head" class="panel-heading"> </div><div id="ctl01_TemplateBody_WebPartManager1_gwpciCornerArt_ciCornerArt_ListTitle_BodyContainer" class="panel-body-container"> <div id="ctl01_TemplateBody_WebPartManager1_gwpciCornerArt_ciCornerArt_ListTitle_Body" class="panel-body"> <div class="TaggedListPanel"> <span><div class='ContentHtml'><link href="https://fonts.googleapis.com/css2?family=Open+Sans:ital,wght@0,300;0,400;0,600;0,700;0,800;1,300;1,400;1,600;1,700;1,800&display=swap" rel="stylesheet" /> <link href="https://fonts.googleapis.com/css2?family=Montserrat:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,500;1,600;1,700;1,900&display=swap" rel="stylesheet" /> <link href="https://fonts.googleapis.com/css2?family=Jost:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,500;1,600;1,700;1,800;1,900&display=swap" rel="stylesheet" /></div><div class='ContentHtml'><style type="text/css"> #ConfContent { color: #0C0C0C; 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padding-top: 0px; padding-bottom: 0px; padding-left: 0; padding-right: 0; line-height: 1em; } #ConfContent h1 { color: #e8ba03; background-color: white; font-family: 'Jost', sans-serif; font-size: 24px; font-weight: 500; text-transform: none; text-align: left; margin-top: 10px; margin-bottom: 10px; margin-left: 0; margin-right: 0; padding-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; line-height: 1.2em; } .ConfContent h1 { color: #e8ba03; background-color: white; font-family: 'Jost', sans-serif; font-size: 24px; font-weight: 500; text-transform: none; text-align: left; margin-top: 10px; margin-bottom: 10px; margin-left: 0; margin-right: 0; padding-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; line-height: 1.2em; } H2 { color: #ffffff; background-color: #9f9f9f; font-family: 'Open Sans', sans-serif; font-size: 18px; text-transform: uppercase; font-weight: 400; display: block; padding-top: 2px; padding-bottom: 2px; padding-left: .33em; padding-right: 3px; 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} .leftbox{ margin-top: 0px; margin-left: auto; margin-right: auto; width: 100% max-width: 100%; object-fit: contain; } .box img{ max-width: 100%; max-height: 100%; display: block; /* remove extra space below image */ object-fit: cover; } .EI21coursegroup_time{ color: #4488DC; font-size: 12pt; font-weight: bold; } .SubmissionLink::before { content: " "; } .SubmissionLink { font-size: 9pt !important; font-family: 'Montserrat', sans-serif; font-weight: 600 !important; text-transform: uppercase !important; color: #d2232a !important; } /* unvisited link */ a.SubmissionLink:link { font-size: 9pt !important; font-family: 'Montserrat', sans-serif; font-weight: 600 !important; text-transform: uppercase !important; color: #d2232a !important; } /* visited link */ a.SubmissionLink:visited { font-size: 9pt !important; font-family: 'Montserrat', sans-serif; font-weight: 600 !important; text-transform: uppercase !important; color: #d2232a !important; } /* mouse over link */ a.SubmissionLink:hover { font-size: 9pt !important; font-family: 'Montserrat', sans-serif; font-weight: 600 !important; text-transform: uppercase !important; color: #d2232a !important; decoration: underline !important; } /* selected link */ a.SubmissionLink:active { font-size: 9pt !important; font-family: 'Montserrat', sans-serif; font-weight: 600 !important; text-transform: uppercase !important; color: #d2232a !important; decoration: underline !important; } .nomarginnopadding { margin: 0; padding: 0; } .SessionOrganizer { margin-left: 27px; } .CommitteeMember { font-size: 13px; font-weight: 500; } .CommitteeMemberAffil { font-size: 13px; font-weight: 400; font-style: normal; } .AwardName { font-weight: 600; color: #3f3f3f; } strong { font-weight: 600; } .BoldItalic { font-weight: 600; font-style: italic; } .Thin { font-weight: 100; } .ExtraLight { font-weight: 200; } .Light { font-weight: 300; } .Regular{ font-weight: 400; } .Medium { font-weight: 500; } .SemiBold { font-weight: 600; } .ExtraBold { font-weight: 800; } .Black { font-weight: 900; 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id="Deadlines"></a> <table class="ImportantDates" align="center"> <thead> <tr> <td colspan="2" style="text-align: center; white-space: nowrap;"><span style="font-weight: 500; font-size: 14px; font-family: Jost, sans-serif; color: #d2232a;">IMPORTANT DATES<br /> </span> <span style="font-size: 10px; color: #d2232a;"><em>Dates currently being confirmed; check back.</em></span> </td> </tr> <tr> <td colspan="2"> </td> </tr> </thead> <tbody> <tr> <td class="ImpDateDescription"> <br /> </td> <td class="impdate"><span style="font-weight: 500;">2022</span></td> </tr> <tr> <td class="ImpDateDescription">Call for Papers Announced</td> <td class="impdate">2 May</td> </tr> <tr> <td class="ImpDateDescription">Journal-first (JIST/JPI) Submissions <br /> </td> <td class="impdate"><br /> </td> </tr> <tr> <td class="ImpDateSubDescription">∙ Submission site Opens</td> <td class="impdatedatesub">2 May </td> </tr> <tr> <td class="ImpDateSubDescription">∙ Journal-first (JIST/JPI) Submissions Due</td> <td 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class="ImpDateDescription">Registration Opens</td> <td class="impdate" style="white-space: nowrap;">1 Dec</td> </tr> <tr> <td class="ImpDateDescription">Demonstration Applications Due</td> <td class="impdate">19 Dec</td> </tr> <tr> <td class="ImpDateDescription">Early Registration Ends</td> <td class="impdate">18 Dec</td> </tr> <tr> <td class="ImpDateDescription"><br /> </td> <td class="impdate" style="text-align: center;"><br /> <span style="font-weight: 500;">2023</span></td> </tr> <tr> <td class="ImpDateDescription">Hotel Reservation Deadline</td> <td class="impdate">6 Jan</td> </tr> <tr> <td class="ImpDateDescription">Symposium begins<br /> </td> <td class="impdate">15 Jan<br /> </td> </tr> <tr> <td class="ImpDateDescription"><br /> </td> <td class="impdate"><br /> </td> </tr> </tbody> </table> </div> </div></div></span> <div translate="yes"> </div> </div> </div> </div> </div><div id="ctl01_TemplateBody_WebPartManager1_gwpciCornerArt_ciCornerArt_downloadContainer" 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It is behind the keynotes It is grey*/ .callout{ background-color:#f2f2f2; padding-top: 10px; padding-right: 10px; padding-bottom: 10px; padding-left: 10px; } /*this is the callout for panels and special events. It is yellow*/ .coloredcallout{ background-color: #fff9e6; padding-top: 10px; padding-right: 10px; padding-bottom: 10px; padding-left: 10px; } /*this is the plenary callout. It used to be pink, now it's light purple*/ .pinkcallout{ background-color: #e6e6ff; padding-top: 10px; padding-right: 10px; padding-bottom: 10px; padding-left: 10px; } #content #story .callout .session_title { line-height: normal; margin-bottom: 1px; } .group { font-size: 14pt; font-weight: bold; text-align: center; } .cat { font-size: 16pt; font-weight: bold; color: red; text-align: center; } .session_time { font-weight: 300; font-size: 13px; font-family: Jost, sans-serif; display: inline-block; margin-bottom: 2ex; } .event_time { font-family: 'Jost', sans-serif; font-size: 14px; font-weight: 400; text-align: center; color: #A2002D; padding-bottom: 10px; } .date { font-family: 'Jost', sans-serif; color: #A2002D; background-color: #ffffff; font-size: 18px; font-weight: 500; text-transform: uppercase; margin-top: 11px; margin-bottom: 10px; margin-left: 0; margin-right: 0; padding-top: 7px; padding-bottom: 7px; padding-left: 0px; padding-right: 0; line-height: 1em; } .session_title { font-family: 'Open Sans', sans-serif; font-size: 14px; color: #3b3b3b; font-weight: 600; margin-top: 0ex; margin-bottom: 0ex; } .session_title:before{ content: ' '; display: block; border: 0; border-bottom: 1px solid #A2002D; background: #A2002D; margin-top: 1ex; margin-bottom: 1ex; } .session_title:after { content: ' '; display: block; border: 0; border-bottom: 1px solid #A2002D; background: #A2002D; margin-top: 1ex; margin-bottom: 1ex; } .chair_label { color: #0C0C0C; font-family: 'Montserrat', sans-serif; font-size: 13px; font-weight: 600; } .chair { color: #0C0C0C; font-family: 'Montserrat', sans-serif; font-size: 13px; font-weight: 400; } p span.author_string { color: #0C0C0C; font-family: 'Montserrat', sans-serif; font-size: 13px; font-weight: 400; font-style: italic; } p span.placeholder_desc { font-size: 12pt; font-weight: normal; } .room { font-family: 'Jost', sans-serif; font-size: 15px; font-weight: 400; text-transform: none; color: #808080; } .redroom { font-family: 'Jost', sans-serif; font-size: 15px; font-weight: 400; text-transform: none; color: #b30000; } .greenroom { font-family: 'Jost', sans-serif; font-size: 15px; font-weight: 400; text-transform: none; color: #00802b; } .blueroom { font-family: 'Jost', sans-serif; font-size: 15px; font-weight: 400; text-transform: none; color: #002db3; } .yellowroom { font-family: 'Jost', sans-serif; font-size: 15px; font-weight: 400; text-transform: none; color: #ffcc00; } .purpleroom { font-family: 'Jost', sans-serif; font-size: 15px; font-weight: 400; text-transform: none; color: #6600CC; } .presentation_title { color: #0C0C0C; font-family: 'Montserrat', sans-serif; font-size: 13px; font-weight: 700; } .presentation_time { color: #0C0C0C; font-family: 'Montserrat', sans-serif; font-size: 13px; font-weight: 500; } .session_notes { color: #0C0C0C; font-weight: 300; font-style: italic; font-size: 14px; font-family: Jost, sans-serif; display: block; margin-bottom: 1ex; } .abstract{ color: #0C0C0C; font-weight: 300; /*font-style: italic;*/ font-size: 13px; font-family: Jost, sans-serif; display: block; margin-bottom: 1ex; } .bio { color: #0C0C0C; font-style: italic; font-weight: 400; display: block; margin-bottom: 1ex; } .abstract_link { color: #2e769e; cursor:pointer; } </style> <h1>Media Watermarking, Security, and Forensics 2023</h1> <p class="date">Monday 16 January 2023</p> <p class="event_time">10:20 – 10:50 AM Coffee Break</p> <p class="event_time">12:30 – 2:00 PM Lunch</p> <div class="pinkcallout"> <p class="session_title">Monday 16 January PLENARY: Neural Operators for Solving PDEs</p> <span class="chair">Session Chair: Robin Jenkin, NVIDIA Corporation (United States)<br> </span> <span class="session_time">2:00 PM – 3:00 PM</span> <br> <span class="room">Cyril Magnin I/II/III<br> </span> <span></span> <p class="session_notes">Deep learning surrogate models have shown promise in modeling complex physical phenomena such as fluid flows, molecular dynamics, and material properties. However, standard neural networks assume finite-dimensional inputs and outputs, and hence, cannot withstand a change in resolution or discretization between training and testing. We introduce Fourier neural operators that can learn operators, which are mappings between infinite dimensional spaces. They are independent of the resolution or grid of training data and allow for zero-shot generalization to higher resolution evaluations. When applied to weather forecasting, neural operators capture fine-scale phenomena and have similar skill as gold-standard numerical weather models for predictions up to a week or longer, while being 4-5 orders of magnitude faster.</p> <br> <span></span> <p class="session_notes"> </p> <span class="author_string"><strong>Anima Anandkumar, </strong>Bren professor, California Institute of Technology, and senior director of AI Research, NVIDIA Corporation (United States)<span class="author_string"></span></span> <p> </p> <span></span> <p class="session_notes">Anima Anandkumar is a Bren Professor at Caltech and Senior Director of AI Research at NVIDIA. She is passionate about designing principled AI algorithms and applying them to interdisciplinary domains. She has received several honors such as the IEEE fellowship, Alfred. P. Sloan Fellowship, NSF Career Award, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network. Anandkumar received her BTech from Indian Institute of Technology Madras, her PhD from Cornell University, and did her postdoctoral research at MIT and assistant professorship at University of California Irvine.</p> </div> <br> <p class="event_time">3:00 – 3:30 PM Coffee Break</p> <span> </span> <div class="pinkcallout"> <p class="session_title">EI 2023 Highlights Session</p> <span class="chair">Session Chair: Robin Jenkin, NVIDIA Corporation (United States)<br> </span> <span class="session_time">3:30 – 5:00 PM</span><br> <span class="room">Cyril Magnin II<br> </span> <p class="session_notes">Join us for a session that celebrates the breadth of what EI has to offer with short papers selected from EI conferences. </p> <p class="session_notes">NOTE: The EI-wide "EI 2023 Highlights" session is concurrent with Monday afternoon COIMG, COLOR, IMAGE, and IQSP conference sessions. </p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>IQSP-309</span> <br> <span class="presentation_title" final_id="IQSP-309" onclick="toggle_me()" style="cursor: pointer;">Evaluation of image quality metrics designed for DRI tasks with automotive cameras, </span><span class="author_string" final_id="IQSP-309" onclick="toggle_me()" style="cursor: pointer;">Valentine Klein, </span><span class="author_string" final_id="IQSP-309" onclick="toggle_me()" style="cursor: pointer;">Yiqi LI, </span><span class="author_string" final_id="IQSP-309" onclick="toggle_me()" style="cursor: pointer;">Claudio Greco, </span><span class="author_string" final_id="IQSP-309" onclick="toggle_me()" style="cursor: pointer;">Laurent Chanas, and </span><span class="author_string" final_id="IQSP-309" onclick="toggle_me()" style="cursor: pointer;">Frédéric Guichard</span><span class="author_string" final_id="IQSP-309" onclick="toggle_me()" style="cursor: pointer;">, DXOMARK (France)</span><span class="abstract_link" final_id="IQSP-309" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="IQSP-309" id="abstract-IQSP-309" onclick="toggle_me()" style="display:none; cursor:pointer;">Driving assistance is increasingly used in new car models. Most driving assistance systems are based on automotive cameras and computer vision. Computer Vision, regardless of the underlying algorithms and technology, requires the images to have good image quality, defined according to the task. This notion of good image quality is still to be defined in the case of computer vision as it has very different criteria than human vision: humans have a better contrast detection ability than image chains. The aim of this article is to compare three different metrics designed for detection of objects with computer vision: the Contrast Detection Probability (CDP) [1, 2, 3, 4], the Contrast Signal to Noise Ratio (CSNR) [5] and the Frequency of Correct Resolution (FCR) [6]. For this purpose, the computer vision task of reading the characters on a license plate will be used as a benchmark. The objective is to check the correlation between the objective metric and the ability of a neural network to perform this task. Thus, a protocol to test these metrics and compare them to the output of the neural network has been designed and the pros and cons of each of these three metrics have been noted.</p> <p> </p> <p class="presentation_time" style="text-align:left;"><span>SD&A-224</span> <br> <span class="presentation_title" final_id="SD&A-224" onclick="toggle_me()" style="cursor: pointer;">Human performance using stereo 3D in a helmet mounted display and association with individual stereo acuity, </span><span class="author_string" final_id="SD&A-224" onclick="toggle_me()" style="cursor: pointer;">Bonnie Posselt</span><span class="author_string" final_id="SD&A-224" onclick="toggle_me()" style="cursor: pointer;">, RAF Centre of Aviation Medicine (United Kingdom)</span><span class="abstract_link" final_id="SD&A-224" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="SD&A-224" id="abstract-SD&A-224" onclick="toggle_me()" style="display:none; cursor:pointer;">Binocular Helmet Mounted Displays (HMDs) are a critical part of the aircraft system, allowing information to be presented to the aviator with stereoscopic 3D (S3D) depth, potentially enhancing situational awareness and improving performance. The utility of S3D in an HMD may be linked to an individual’s ability to perceive changes in binocular disparity (stereo acuity). Though minimum stereo acuity standards exist for most military aviators, current test methods may be unable to characterise this relationship. This presentation will investigate the effect of S3D on performance when used in a warning alert displayed in an HMD. Furthermore, any effect on performance, ocular symptoms, and cognitive workload shall be evaluated in regard to individual stereo acuity measured with a variety of paper-based and digital stereo tests.</p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>IMAGE-281</span> <br> <span class="presentation_title" final_id="IMAGE-281" onclick="toggle_me()" style="cursor: pointer;">Smartphone-enabled point-of-care blood hemoglobin testing with color accuracy-assisted spectral learning, </span><span class="author_string" final_id="IMAGE-281" onclick="toggle_me()" style="cursor: pointer;">Sang Mok Park<sup>1</sup>, </span><span class="author_string" final_id="IMAGE-281" onclick="toggle_me()" style="cursor: pointer;">Yuhyun Ji<sup>1</sup>, </span><span class="author_string" final_id="IMAGE-281" onclick="toggle_me()" style="cursor: pointer;">Semin Kwon<sup>1</sup>, </span><span class="author_string" final_id="IMAGE-281" onclick="toggle_me()" style="cursor: pointer;">Andrew R. O’Brien<sup>2</sup>, </span><span class="author_string" final_id="IMAGE-281" onclick="toggle_me()" style="cursor: pointer;">Ying Wang<sup>2</sup>, and </span><span class="author_string" final_id="IMAGE-281" onclick="toggle_me()" style="cursor: pointer;">Young L. Kim<sup>1</sup></span><span class="author_string" final_id="IMAGE-281" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Purdue University and <sup>2</sup>Indiana University School of Medicine (United States)</span><span class="abstract_link" final_id="IMAGE-281" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="IMAGE-281" id="abstract-IMAGE-281" onclick="toggle_me()" style="display:none; cursor:pointer;">We develop an mHealth technology for noninvasively measuring blood Hgb levels in patients with sickle cell anemia, using the photos of peripheral tissue acquired by the built-in camera of a smartphone. As an easily accessible sensing site, the inner eyelid (i.e., palpebral conjunctiva) is used because of the relatively uniform microvasculature and the absence of skin pigments. Color correction (color reproduction) and spectral learning (spectral super-resolution spectroscopy) algorithms are integrated for accurate and precise mHealth blood Hgb testing. First, color correction using a color reference chart with multiple color patches extracts absolute color information of the inner eyelid, compensating for smartphone models, ambient light conditions, and data formats during photo acquisition. Second, spectral learning virtually transforms the smartphone camera into a hyperspectral imaging system, mathematically reconstructing high-resolution spectra from color-corrected eyelid images. Third, color correction and spectral learning algorithms are combined with a spectroscopic model for blood Hgb quantification among sickle cell patients. Importantly, single-shot photo acquisition of the inner eyelid using the color reference chart allows straightforward, real-time, and instantaneous reading of blood Hgb levels. Overall, our mHealth blood Hgb tests could potentially be scalable, robust, and sustainable in resource-limited and homecare settings.</p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>AVM-118</span> <br> <span class="presentation_title" final_id="AVM-118" onclick="toggle_me()" style="cursor: pointer;">Designing scenes to quantify the performance of automotive perception systems, </span><span class="author_string" final_id="AVM-118" onclick="toggle_me()" style="cursor: pointer;">Zhenyi Liu<sup>1</sup>, </span><span class="author_string" final_id="AVM-118" onclick="toggle_me()" style="cursor: pointer;">Devesh Shah<sup>2</sup>, </span><span class="author_string" final_id="AVM-118" onclick="toggle_me()" style="cursor: pointer;">Alireza Rahimpour<sup>2</sup>, </span><span class="author_string" final_id="AVM-118" onclick="toggle_me()" style="cursor: pointer;">Joyce Farrell<sup>1</sup>, and </span><span class="author_string" final_id="AVM-118" onclick="toggle_me()" style="cursor: pointer;">Brian Wandell<sup>1</sup></span><span class="author_string" final_id="AVM-118" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Stanford University and <sup>2</sup>Ford Motor Company (United States)</span><span class="abstract_link" final_id="AVM-118" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="AVM-118" id="abstract-AVM-118" onclick="toggle_me()" style="display:none; cursor:pointer;">We implemented an end-to-end simulation for perception systems, based on cameras, that are used in automotive applications. The open-source software creates complex driving scenes and simulates cameras that acquire images of these scenes. The camera images are then used by a neural network in the perception system to identify the locations of scene objects, providing the results as input to the decision system. In this paper, we design collections of test scenes that can be used to quantify the perception system’s performance under a range of (a) environmental conditions (object distance, occlusion ratio, lighting levels), and (b) camera parameters (pixel size, lens type, color filter array). We are designing scene collections to analyze performance for detecting vehicles, traffic signs and vulnerable road users in a range of environmental conditions and for a range of camera parameters. With experience, such scene collections may serve a role similar to that of standardized test targets that are used to quantify camera image quality (e.g., acuity, color).</p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>VDA-403</span> <br> <span class="presentation_title" final_id="VDA-403" onclick="toggle_me()" style="cursor: pointer;">Visualizing and monitoring the process of injection molding, </span><span class="author_string" final_id="VDA-403" onclick="toggle_me()" style="cursor: pointer;">Christian A. Steinparz<sup>1</sup>, </span><span class="author_string" final_id="VDA-403" onclick="toggle_me()" style="cursor: pointer;">Thomas Mitterlehner<sup>2</sup>, </span><span class="author_string" final_id="VDA-403" onclick="toggle_me()" style="cursor: pointer;">Bernhard Praher<sup>2</sup>, </span><span class="author_string" final_id="VDA-403" onclick="toggle_me()" style="cursor: pointer;">Klaus Straka<sup>1,</sup><sup>2</sup>, </span><span class="author_string" final_id="VDA-403" onclick="toggle_me()" style="cursor: pointer;">Holger Stitz<sup>1,</sup><sup>3</sup>, and </span><span class="author_string" final_id="VDA-403" onclick="toggle_me()" style="cursor: pointer;">Marc Streit<sup>1,</sup><sup>3</sup></span><span class="author_string" final_id="VDA-403" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Johannes Kepler University, <sup>2</sup>Moldsonics GmbH, and <sup>3</sup>datavisyn GmbH (Austria)</span><span class="abstract_link" final_id="VDA-403" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="VDA-403" id="abstract-VDA-403" onclick="toggle_me()" style="display:none; cursor:pointer;">In injection molding machines the molds are rarely equipped with sensor systems. The availability of non-invasive ultrasound-based in-mold sensors provides better means for guiding operators of injection molding machines throughout the production process. However, existing visualizations are mostly limited to plots of temperature and pressure over time. In this work, we present the result of a design study created in collaboration with domain experts. The resulting prototypical application uses real-world data taken from live ultrasound sensor measurements for injection molding cavities captured over multiple cycles during the injection process. Our contribution includes a definition of tasks for setting up and monitoring the machines during the process, and the corresponding web-based visual analysis tool addressing these tasks. The interface consists of a multi-view display with various levels of data aggregation that is updated live for newly streamed data of ongoing injection cycles.</p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>COIMG-155</span> <br> <span class="presentation_title" final_id="COIMG-155" onclick="toggle_me()" style="cursor: pointer;">Commissioning the James Webb Space Telescope, </span><span class="author_string" final_id="COIMG-155" onclick="toggle_me()" style="cursor: pointer;">Joseph M. Howard</span><span class="author_string" final_id="COIMG-155" onclick="toggle_me()" style="cursor: pointer;">, NASA Goddard Space Flight Center (United States)</span><span class="abstract_link" final_id="COIMG-155" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="COIMG-155" id="abstract-COIMG-155" onclick="toggle_me()" style="display:none; cursor:pointer;">Astronomy is arguably in a golden age, where current and future NASA space telescopes are expected to contribute to this rapid growth in understanding of our universe. The most recent addition to our space-based telescopes dedicated to astronomy and astrophysics is the James Webb Space Telescope (JWST), which launched on 25 December 2021. This talk will discuss the first six months in space for JWST, which were spent commissioning the observatory with many deployments, alignments, and system and instrumentation checks. These engineering activities help verify the proper working of the telescope prior to commencing full science operations. For the session: Computational Imaging using Fourier Ptychography and Phase Retrieval.</p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>HVEI-223</span> <br> <span class="presentation_title" final_id="HVEI-223" onclick="toggle_me()" style="cursor: pointer;">Critical flicker frequency (CFF) at high luminance levels, </span><span class="author_string" final_id="HVEI-223" onclick="toggle_me()" style="cursor: pointer;">Alexandre Chapiro<sup>1</sup>, </span><span class="author_string" final_id="HVEI-223" onclick="toggle_me()" style="cursor: pointer;">Nathan Matsuda<sup>1</sup>, </span><span class="author_string" final_id="HVEI-223" onclick="toggle_me()" style="cursor: pointer;">Maliha Ashraf<sup>2</sup>, and </span><span class="author_string" final_id="HVEI-223" onclick="toggle_me()" style="cursor: pointer;">Rafal Mantiuk<sup>3</sup></span><span class="author_string" final_id="HVEI-223" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Meta (United States), <sup>2</sup>University of Liverpool (United Kingdom), and <sup>3</sup>University of Cambridge (United Kingdom)</span><span class="abstract_link" final_id="HVEI-223" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="HVEI-223" id="abstract-HVEI-223" onclick="toggle_me()" style="display:none; cursor:pointer;">The critical flicker fusion (CFF) is the frequency of changes at which a temporally periodic light will begin to appear completely steady to an observer. This value is affected by several visual factors, such as the luminance of the stimulus or its location on the retina. With new high dynamic range (HDR) displays, operating at higher luminance levels, and virtual reality (VR) displays, presenting at wide fields-of-view, the effective CFF may change significantly from values expected for traditional presentation. In this work we use a prototype HDR VR display capable of luminances up to 20,000 cd/m^2 to gather a novel set of CFF measurements for never before examined levels of luminance, eccentricity, and size. Our data is useful to study the temporal behavior of the visual system at high luminance levels, as well as setting useful thresholds for display engineering.</p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>HPCI-228</span> <br> <span class="presentation_title" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Physics guided machine learning for image-based material decomposition of tissues from simulated breast models with calcifications, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Muralikrishnan Gopalakrishnan Meena<sup>1</sup>, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Amir K. Ziabari<sup>1</sup>, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Singanallur Venkatakrishnan<sup>1</sup>, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Isaac R. Lyngaas<sup>1</sup>, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Matthew R. Norman<sup>1</sup>, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Balint Joo<sup>1</sup>, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Thomas L. Beck<sup>1</sup>, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Charles A. Bouman<sup>2</sup>, </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Anuj Kapadia<sup>1</sup>, and </span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">Xiao Wang<sup>1</sup></span><span class="author_string" final_id="HPCI-228" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Oak Ridge National Laboratory and <sup>2</sup>Purdue University (United States)</span><span class="abstract_link" final_id="HPCI-228" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="HPCI-228" id="abstract-HPCI-228" onclick="toggle_me()" style="display:none; cursor:pointer;">Material decomposition of Computed Tomography (CT) scans using projection-based approaches, while highly accurate, poses a challenge for medical imaging researchers and clinicians due to limited or no access to projection data. We introduce a deep learning image-based material decomposition method guided by physics and requiring no access to projection data. The method is demonstrated to decompose tissues from simulated dual-energy X-ray CT scans of virtual human phantoms containing four materials - adipose, fibroglandular, calcification, and air. The method uses a hybrid unsupervised and supervised learning technique to tackle the material decomposition problem. We take advantage of the unique X-ray absorption rate of calcium compared to body tissues to perform a preliminary segmentation of calcification from the images using unsupervised learning. We then perform supervised material decomposition using a deep learned UNET model which is trained using GPUs in the high-performant systems at the Oak Ridge Leadership Computing Facility. The method is demonstrated on simulated breast models to decompose calcification, adipose, fibroglandular, and air.</p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>3DIA-104</span> <br> <span class="presentation_title" final_id="3DIA-104" onclick="toggle_me()" style="cursor: pointer;">Layered view synthesis for general images, </span><span class="author_string" final_id="3DIA-104" onclick="toggle_me()" style="cursor: pointer;">Loïc Dehan, </span><span class="author_string" final_id="3DIA-104" onclick="toggle_me()" style="cursor: pointer;">Wiebe Van Ranst, and </span><span class="author_string" final_id="3DIA-104" onclick="toggle_me()" style="cursor: pointer;">Patrick Vandewalle</span><span class="author_string" final_id="3DIA-104" onclick="toggle_me()" style="cursor: pointer;">, Katholieke University Leuven (Belgium)</span><span class="abstract_link" final_id="3DIA-104" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="3DIA-104" id="abstract-3DIA-104" onclick="toggle_me()" style="display:none; cursor:pointer;">We describe a novel method for monocular view synthesis. The goal of our work is to create a visually pleasing set of horizontally spaced views based on a single image. This can be applied in view synthesis for virtual reality and glasses-free 3D displays. Previous methods produce realistic results on images that show a clear distinction between a foreground object and the background. We aim to create novel views in more general, crowded scenes in which there is no clear distinction. Our main contributions are a computationally efficient method for realistic occlusion inpainting and blending, especially in complex scenes. Our method can be effectively applied to any image, which is shown both qualitatively and quantitatively on a large dataset of stereo images. Our method performs natural disocclusion inpainting and maintains the shape and edge quality of foreground objects.</p> <p> </p> <p class="presentation_time" style="text-align:left;"> <span>ISS-329</span> <br> <span class="presentation_title" final_id="ISS-329" onclick="toggle_me()" style="cursor: pointer;">A self-powered asynchronous image sensor with independent in-pixel harvesting and sensing operations, </span><span class="author_string" final_id="ISS-329" onclick="toggle_me()" style="cursor: pointer;">Ruben Gomez-Merchan, </span><span class="author_string" final_id="ISS-329" onclick="toggle_me()" style="cursor: pointer;">Juan Antonio Leñero-Bardallo, and </span><span class="author_string" final_id="ISS-329" onclick="toggle_me()" style="cursor: pointer;">Ángel Rodríguez-Vázquez</span><span class="author_string" final_id="ISS-329" onclick="toggle_me()" style="cursor: pointer;">, University of Seville (Spain)</span><span class="abstract_link" final_id="ISS-329" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="ISS-329" id="abstract-ISS-329" onclick="toggle_me()" style="display:none; cursor:pointer;">A new self-powered asynchronous sensor with a novel pixel architecture is presented. Pixels are autonomous and can harvest or sense energy independently. During the image acquisition, pixels toggle to a harvesting operation mode once they have sensed their local illumination level. With the proposed pixel architecture, most illuminated pixels provide an early contribution to power the sensor, while low illuminated ones spend more time sensing their local illumination. Thus, the equivalent frame rate is higher than the offered by conventional self-powered sensors that harvest and sense illumination in independient phases. The proposed sensor uses a Time-to-First-Spike readout that allows trading between image quality and data and bandwidth consumption. The sensor has HDR operation with a dynamic range of 80 dB. Pixel power consumption is only 70 pW. In the article, we describe the sensor’s and pixel’s architectures in detail. Experimental results are provided and discussed. Sensor specifications are benchmarked against the art.</p> <p> </p> <p class="presentation_time" style="text-align:left;"><span>COLOR-184</span> <br> <span class="presentation_title" final_id="COLOR-184" onclick="toggle_me()" style="cursor: pointer;">Color blindness and modern board games, </span><span class="author_string" final_id="COLOR-184" onclick="toggle_me()" style="cursor: pointer;">Alessandro Rizzi<sup>1</sup> and </span><span class="author_string" final_id="COLOR-184" onclick="toggle_me()" style="cursor: pointer;">Matteo Sassi<sup>2</sup></span><span class="author_string" final_id="COLOR-184" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Università degli Studi di Milano and <sup>2</sup>consultant (Italy)</span><span class="abstract_link" final_id="COLOR-184" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="COLOR-184" id="abstract-COLOR-184" onclick="toggle_me()" style="display:none; cursor:pointer;">Board game industry is experiencing a strong renewed interest. In the last few years, about 4000 new board games have been designed and distributed each year. Board game players gender balance is reaching the equality, but nowadays the male component is a slight majority. This means that (at least) around 10% of board game players are color blind. How does the board game industry deal with this ? Recently, a raising of awareness in the board game design has started but so far there is a big gap compared with (e.g.) the computer game industry. This paper presents some data about the actual situation, discussing exemplary cases of successful board games.</p> <p> </p> <script> function toggle_me() { var elm = event.srcElement var final_id = elm.getAttribute("final_id") var the_id = "abstract-" + final_id; var x = document.getElementById(the_id); if (x.style.display === "none"){ x.style.display = "block"; } else { x.style.display = "none"; } }</script> </div> <br> <span> </span> <p class="event_time">5:00 – 6:15 PM EI 2023 All-Conference Welcome Reception (in the Cyril Magnin Foyer)</p> <p class="date">Tuesday 17 January 2023</p> <p class="session_title">Audio Attribution & Recognition (T1.1)</p> <span class="chair_label">Session Chair: </span> <span class="chair">Gaurav Sharma, University of Rochester (United States)<br> </span> <span class="session_time">9:00 – 9:50 AM</span> <br> <span class="room">Mission I<br> </span> <p class="presentation_time" style="text-align:left;">9:00<a name="MWSF-372"></a><span style="float: right;">MWSF-372</span> <br> <span class="presentation_title" final_id="MWSF-372" onclick="toggle_me()" style="cursor: pointer;">Synthetic speech attribution using self-supervised audio spectrogram transformer, </span><span class="author_string" final_id="MWSF-372" onclick="toggle_me()" style="cursor: pointer;">Amit Kumar Singh Yadav, </span><span class="author_string" final_id="MWSF-372" onclick="toggle_me()" style="cursor: pointer;">Emily R. Bartusiak, </span><span class="author_string" final_id="MWSF-372" onclick="toggle_me()" style="cursor: pointer;">Kratika Bhagtani, and </span><span class="author_string" final_id="MWSF-372" onclick="toggle_me()" style="cursor: pointer;">Edward J. Delp</span><span class="author_string" final_id="MWSF-372" onclick="toggle_me()" style="cursor: pointer;">, Purdue University (United States)</span><span class="abstract_link" final_id="MWSF-372" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-372" id="abstract-MWSF-372" onclick="toggle_me()" style="display:none; cursor:pointer;">The ability to create convincing human voices is within everyone’s reach due to the availability of speech generation tools. This necessitates the development of forensics methods that authenticate and attribute speech signals. In this paper, we examine a speech attribution task, which entails identifying the origin of a speech signal. Our proposed approach converts speech signals into mel spectrograms and uses a self-supervised pretrained transformer for attribution. This transformer, known as the Self-Supervised Audio Spectrogram Transformer (SSAST), is first pretrained on two large audio datasets: Audio Set and LibriSpeech. We finetune SSAST on two other datasets: ASVspoof2019 and the 2022 IEEE SP Cup dataset. ASVspoof2019 has 18 classes (1 authentic speech class and 17 classes corresponding to different speech generation methods), while the 2022 IEEE SP Cup dataset has 5 classes (all are speech generation methods). Our approach achieves high closed-set accuracy on both datasets (99.8% and 96.3%, respectively). Additionally, we investigate the method’s ability to generalize to unknown speech generation methods. We again demonstrate high success, achieving 90.2% open-set accuracy on ASVspoof2019. Finally, we show that our approach is robust to typical compression rates used by YouTube.</p> <p> </p> <p class="presentation_time" style="text-align:left;">9:25<a name="MWSF-373"></a><span style="float: right;">MWSF-373</span> <br> <span class="presentation_title" final_id="MWSF-373" onclick="toggle_me()" style="cursor: pointer;">Audio captcha breaking and consequences for human users, </span><span class="author_string" final_id="MWSF-373" onclick="toggle_me()" style="cursor: pointer;">Martin Steinebach<sup>1</sup>, </span><span class="author_string" final_id="MWSF-373" onclick="toggle_me()" style="cursor: pointer;">Fabian Oberthür<sup>2</sup>, </span><span class="author_string" final_id="MWSF-373" onclick="toggle_me()" style="cursor: pointer;">Verena Battis<sup>1</sup>, and </span><span class="author_string" final_id="MWSF-373" onclick="toggle_me()" style="cursor: pointer;">York Yannikos<sup>1</sup></span><span class="author_string" final_id="MWSF-373" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Fraunhofer SIT and <sup>2</sup>TU Darmstadt (Germany)</span><span class="abstract_link" final_id="MWSF-373" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-373" id="abstract-MWSF-373" onclick="toggle_me()" style="display:none; cursor:pointer;">On the Internet, humans must repeatedly identify themselves to gain access to information or to use services. To check whether a request is sent by a human being and not by a computer, a task must be solved. This task is called a captcha. Tasks like automated OSINT require automatic solving of these captchas. We investigate solving of audio captchas. For this purpose, a program is written that integrates two common speech-to-text methods. The program achieves very good results and reaches an accuracy of about 81 percent. As captchas are also an important tool for Internet access security, we also use the results of our attack to make suggestions for improving the security of these captchas. We compare compares human listeners with computers and reveal weaknesses of audio captchas.</p> <p> </p> <br> <br> <p class="session_title">Steganography & Fingerprinting (T1.2)</p> <span class="chair_label">Session Chair: </span> <span class="chair">Jessica Fridrich, Binghamton University (United States)<br> </span> <span class="session_time">9:50 – 10:15 AM</span> <br> <span class="room">Mission I<br> </span> <p class="presentation_time" style="text-align:left;">9:50<a name="MWSF-374"></a><span style="float: right;">MWSF-374</span> <br> <span class="presentation_title" final_id="MWSF-374" onclick="toggle_me()" style="cursor: pointer;">Cost polarization by dequantizing for JPEG steganography, </span><span class="author_string" final_id="MWSF-374" onclick="toggle_me()" style="cursor: pointer;">Edgar Kaziakhmedov, </span><span class="author_string" final_id="MWSF-374" onclick="toggle_me()" style="cursor: pointer;">Eli Dworetzky, </span><span class="author_string" final_id="MWSF-374" onclick="toggle_me()" style="cursor: pointer;">Yassine Yousfi, and </span><span class="author_string" final_id="MWSF-374" onclick="toggle_me()" style="cursor: pointer;">Jessica Fridrich</span><span class="author_string" final_id="MWSF-374" onclick="toggle_me()" style="cursor: pointer;">, Binghamton University (United States)</span><span class="abstract_link" final_id="MWSF-374" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-374" id="abstract-MWSF-374" onclick="toggle_me()" style="display:none; cursor:pointer;">In this article, we study a recently proposed method for improving empirical security of steganography in JPEG images in which the sender starts with an additive embedding scheme with symmetrical costs of ± 1 changes and then decreases the cost of one of these changes based on an image obtained by applying a deblocking (JPEG dequantization) algorithm to the cover JPEG. This approach provides rather significant gains in security at negligible embedding complexity overhead for a wide range of quality factors and across various embedding schemes. Challenging the original explanation of the inventors of this idea, which is based on interpreting the dequantized image as an estimate of the precover (uncompressed) image, we provide alternative arguments. The key observation and the main reason why this approach works is how the polarizations of individual DCT coefficients work together. By using a MiPOD model of content complexity of the uncompressed cover image, we show that the cost polarization technique decreases the chances of “bad” combinations of embedding changes that would likely be introduced by the original scheme with symmetric costs. This statement is quantified by computing the likelihood of the stego image w.r.t. the multivariate Gaussian precover distribution in DCT domain. Furthermore, it is shown that the cost polarization decreases spatial discontinuities between blocks (blockiness) in the stego image and enforces desirable correlations of embedding changes across blocks. To further prove the point, it is shown that in a source that adheres to the precover model, a simple Wiener filter can serve equally well as a deep-learning based deblocker</p> <p> </p> <br> <br> <p class="event_time">10:00 AM – 7:30 PM Industry Exhibition - Tuesday (in the Cyril Magnin Foyer)</p> <p class="event_time">10:20 – 10:50 AM Coffee Break</p> <p class="session_title">Steganography & Fingerprinting (T2.1)</p> <span class="chair_label">Session Chair: </span> <span class="chair">Jessica Fridrich, Binghamton University (United States)<br> </span> <span class="session_time">10:55 – 11:20 AM</span> <br> <span class="room">Mission I<br> </span> <p class="presentation_time" style="text-align:left;">10:55<a name="MWSF-375"></a><span style="float: right;">MWSF-375</span> <br> <span class="presentation_title" final_id="MWSF-375" onclick="toggle_me()" style="cursor: pointer;">Predicting positions of flipped bits in robust image hashes, </span><span class="author_string" final_id="MWSF-375" onclick="toggle_me()" style="cursor: pointer;">Martin Steinebach<sup>1</sup>, </span><span class="author_string" final_id="MWSF-375" onclick="toggle_me()" style="cursor: pointer;">Niklas Bunzel<sup>1</sup>, </span><span class="author_string" final_id="MWSF-375" onclick="toggle_me()" style="cursor: pointer;">Marius Hammann<sup>2</sup>, and </span><span class="author_string" final_id="MWSF-375" onclick="toggle_me()" style="cursor: pointer;">Huajian Liu<sup>1</sup></span><span class="author_string" final_id="MWSF-375" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Fraunhofer Institute for Secure Information Technology and <sup>2</sup>TU Darmstadt (Germany)</span><span class="abstract_link" final_id="MWSF-375" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-375" id="abstract-MWSF-375" onclick="toggle_me()" style="display:none; cursor:pointer;">Both robust and cryptographic hash methods have advantages and disadvantages. It would be ideal if robustness and cryptographic confidentiality could be combined. The problem here is that the concept of similarity of robust hashes cannot be applied to cryptographic hashes. Therefore, methods must be developed to reliably intercept the degrees of freedom of robust hashes before they are included in a cryptographic hash, but without losing their robustness. To achieve this, we need to predict the bits of a hash that are most likely to be modified, for example after a JPEG compression. We show that machine learning can be used to make a much more reliable prediction than the approaches previously discussed in the literature.</p> <p> </p> <br> <br> <p class="session_title">Watermarking (T2.2)</p> <span class="chair_label">Session Chair: </span> <span class="chair">Adnan Alattar, Digimarc Corporation (United States)<br> </span> <span class="session_time">11:20 AM – 12:10 PM</span> <br> <span class="room">Mission I<br> </span> <p class="presentation_time" style="text-align:left;">11:20<a name="MWSF-376"></a><span style="float: right;">MWSF-376</span> <br> <span class="presentation_title" final_id="MWSF-376" onclick="toggle_me()" style="cursor: pointer;">LECA: A learned approach for efficient cover-agnostic watermarking, </span><span class="author_string" final_id="MWSF-376" onclick="toggle_me()" style="cursor: pointer;">Xiyang Luo, </span><span class="author_string" final_id="MWSF-376" onclick="toggle_me()" style="cursor: pointer;">Michael Goebel, </span><span class="author_string" final_id="MWSF-376" onclick="toggle_me()" style="cursor: pointer;">Elnaz Barshan, and </span><span class="author_string" final_id="MWSF-376" onclick="toggle_me()" style="cursor: pointer;">Feng Yang</span><span class="author_string" final_id="MWSF-376" onclick="toggle_me()" style="cursor: pointer;">, Google LLC (United States)</span><span class="abstract_link" final_id="MWSF-376" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-376" id="abstract-MWSF-376" onclick="toggle_me()" style="display:none; cursor:pointer;">In this work, we present an efficient multi-bit deep image watermarking method that is cover-agnostic yet also robust to geometric distortions such as translation and scaling as well as other distortions such as JPEG compression and noise. Our design consists of a light-weight watermark encoder jointly trained with a deep neural network based decoder. Such a design allows us to retain the efficiency of the encoder while fully utilizing the power of a deep neural network. Moreover, the watermark encoder is independent of the image content, allowing users to pre-generate the watermarks for further efficiency. To offer robustness towards geometric transformations, we introduced a learned model for predicting the scale and offset of the watermarked images. Moreover, our watermark encoder is independent of the image content, making the generated watermarks universally applicable to different cover images. Experiments show that our method outperforms comparably efficient watermarking methods by a large margin.</p> <p> </p> <p class="presentation_time" style="text-align:left;">11:45<a name="MWSF-377"></a><span style="float: right;">MWSF-377</span> <br> <span class="presentation_title" final_id="MWSF-377" onclick="toggle_me()" style="cursor: pointer;">Privacy preserving leak detection in peer-to-peer communication, </span><span class="author_string" final_id="MWSF-377" onclick="toggle_me()" style="cursor: pointer;">Julian Heeger, </span><span class="author_string" final_id="MWSF-377" onclick="toggle_me()" style="cursor: pointer;">Simon Bugert, </span><span class="author_string" final_id="MWSF-377" onclick="toggle_me()" style="cursor: pointer;">Waldemar Berchtold, </span><span class="author_string" final_id="MWSF-377" onclick="toggle_me()" style="cursor: pointer;">Alexander Gruler, and </span><span class="author_string" final_id="MWSF-377" onclick="toggle_me()" style="cursor: pointer;">Martin Steinebach</span><span class="author_string" final_id="MWSF-377" onclick="toggle_me()" style="cursor: pointer;">, Fraunhofer Institute for Secure Information Technology (Germany)</span><span class="abstract_link" final_id="MWSF-377" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-377" id="abstract-MWSF-377" onclick="toggle_me()" style="display:none; cursor:pointer;">During the pandemic the usage of video platforms skyrocketed among office workers and students and even today, when more and more events are held on-site again, the usage of video platforms is at an all-time high. However, the many advantages of these platforms cannot hide some problems. In the professional field, the publication of audio recordings without the consent of the author can get him into trouble. In education, another problem is bullying. The distance from the victim lowers the inhibition threshold for bullying, which means that platforms need tools to combat it. In this work, we present a system, which can not only identify the person leaking the footage, but also identify all other persons present in the footage. This system can be used in both described scenarios.</p> <p> </p> <br> <br> <p class="event_time">12:30 – 2:00 PM Lunch</p> <div class="pinkcallout"> <p class="session_title">Tuesday 17 January PLENARY: Embedded Gain Maps for Adaptive Display of High Dynamic Range Images</p> <span class="chair">Session Chair: Robin Jenkin, NVIDIA Corporation (United States)<br> </span> <span class="session_time">2:00 PM – 3:00 PM</span> <br> <span class="room">Cyril Magnin I/II/III<br> </span> <span></span> <p class="session_notes">Images optimized for High Dynamic Range (HDR) displays have brighter highlights and more detailed shadows, resulting in an increased sense of realism and greater impact. However, a major issue with HDR content is the lack of consistency in appearance across different devices and viewing environments. There are several reasons, including varying capabilities of HDR displays and the different tone mapping methods implemented across software and platforms. Consequently, HDR content authors can neither control nor predict how their images will appear in other apps.</p> <span></span> <p class="session_notes">We present a flexible system that provides consistent and adaptive display of HDR images. Conceptually, the method combines both SDR and HDR renditions within a single image and interpolates between the two dynamically at display time. We compute a Gain Map that represents the difference between the two renditions. In the file, we store a Base rendition (either SDR or HDR), the Gain Map, and some associated metadata. At display time, we combine the Base image with a scaled version of the Gain Map, where the scale factor depends on the image metadata, the HDR capacity of the display, and the viewing environment. </p> <br> <span></span> <p class="session_notes"> </p> <span class="author_string"><strong>Eric Chan, </strong>Fellow, Adobe Inc. (United States)<span class="author_string"></span></span> <p> </p> <span></span> <p class="session_notes">Eric Chan is a Fellow at Adobe, where he develops software for editing photographs. Current projects include Photoshop, Lightroom, Camera Raw, and Digital Negative (DNG). When not writing software, Chan enjoys spending time at his other keyboard, the piano. He is an enthusiastic nature photographer and often combines his photo activities with travel and hiking.</p> <br> <span class="author_string"><strong>Paul M. Hubel, </strong>director of Image Quality in Software Engineering, Apple Inc. (United States)<span class="author_string"></span></span> <p> </p> <span></span> <p class="session_notes">Paul M. Hubel is director of Image Quality in Software Engineering at Apple. He has worked on computational photography and image quality of photographic systems for many years on all aspects of the imaging chain, particularly for iPhone. He trained in optical engineering at University of Rochester, Oxford University, and MIT, and has more than 50 patents on color imaging and camera technology. Hubel is active on the ISO-TC42 committee Digital Photography, where this work is under discussion, and is currently a VP on the IS&T Board. Outside work he enjoys photography, travel, cycling, coffee roasting, and plays trumpet in several bay area ensembles.</p> </div> <br> <p class="event_time">3:00 – 3:30 PM Coffee Break</p> <p class="session_title">Deepfake Detection (T3)</p> <span class="chair_label">Session Chairs: </span> <span class="chair">Adnan Alattar, Digimarc Corporation (United States) and Gaurav Sharma, University of Rochester (United States)<br> </span> <span class="session_time">3:30 – 5:15 PM</span> <br> <span class="room">Mission I<br> </span> <p class="presentation_time" style="text-align:left;">3:30<a name="MWSF-378"></a><span style="float: right;">MWSF-378</span> <br> <span class="presentation_title" final_id="MWSF-378" onclick="toggle_me()" style="cursor: pointer;">Pros and cons of comparing and combining hand-crafted and neural network based DeepFake detection based on eye blinking behavior, </span><span class="author_string" final_id="MWSF-378" onclick="toggle_me()" style="cursor: pointer;">Dennis Siegel, </span><span class="author_string" final_id="MWSF-378" onclick="toggle_me()" style="cursor: pointer;">Stefan Seidlitz, </span><span class="author_string" final_id="MWSF-378" onclick="toggle_me()" style="cursor: pointer;">Christian Krätzer, and </span><span class="author_string" final_id="MWSF-378" onclick="toggle_me()" style="cursor: pointer;">Jana Dittmann</span><span class="author_string" final_id="MWSF-378" onclick="toggle_me()" style="cursor: pointer;">, Otto-von-Guericke University Magdeburg (Germany)</span><span class="abstract_link" final_id="MWSF-378" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-378" id="abstract-MWSF-378" onclick="toggle_me()" style="display:none; cursor:pointer;">DeepFakes are a recent trend in computer vision, posing a thread to authenticity of digital media. For the detection of DeepFakes most prominently neural network based approaches are used. Those detectors often lack explanatory power on why the given decision was made, due to their black-box nature. Furthermore, taking the social, ethical and legal perspective (e.g. the upcoming European Commission in the Artificial Intelligence Act) into account, black-box decision methods should be avoided and Human Oversight should be guaranteed. In terms of explainability of AI systems, many approaches work based on post-hoc visualization methods (e.g. by back-propagation) or the reduction of complexity. In our paper a different approach is used, combining hand-crafted as well as neural network based components analyzing the same phenomenon to aim for explainability. The exemplary chosen semantic phenomenon analyzed here is the eye blinking behavior in a genuine or DeepFake video. Furthermore, the impact of video duration on the classification result is evaluated empirically, so that a minimum duration threshold can be set to reasonably detect DeepFakes.</p> <p> </p> <p class="presentation_time" style="text-align:left;">3:55<a name="MWSF-379"></a><span style="float: right;">MWSF-379</span> <br> <span class="presentation_title" final_id="MWSF-379" onclick="toggle_me()" style="cursor: pointer;">Human-in-control and quality assurance aspects for a benchmarking framework for DeepFake detection models based on hand-crafted and learned feature spaces, </span><span class="author_string" final_id="MWSF-379" onclick="toggle_me()" style="cursor: pointer;">Christian Krätzer, </span><span class="author_string" final_id="MWSF-379" onclick="toggle_me()" style="cursor: pointer;">Dennis Siegel, </span><span class="author_string" final_id="MWSF-379" onclick="toggle_me()" style="cursor: pointer;">Stefan Seidlitz, and </span><span class="author_string" final_id="MWSF-379" onclick="toggle_me()" style="cursor: pointer;">Jana Dittmann</span><span class="author_string" final_id="MWSF-379" onclick="toggle_me()" style="cursor: pointer;">, Otto-von-Guericke University Magdeburg (Germany)</span><span class="abstract_link" final_id="MWSF-379" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-379" id="abstract-MWSF-379" onclick="toggle_me()" style="display:none; cursor:pointer;">DeepFakes, as a novel video manipulation technique replacing persons in video and audio files or -streams, provide a significant threat to our media-driven world. As recent events have shown, suddenly footage seen on news portals and social media channels must be viewed much more critically, as the necessary skills and tools to create such DeepFakes have become very easily available. As a consequence, a research field focusing on DeepFake detection has been established roughly in 2017 and has grown into a hot research topic with virtually thousands of research publications in the last five years. Unfortunately, most of these publications focus solely on designing new detectors and in-house evaluations of their performance. Few publications pay respect to the fact that, for making forensic methods such as DeepFake detectors field-ready for forensic investigations, they would have to be integrated as quality assured methods with precisely known error rates into forensic processing pipelines. Our paper addresses detector benchmarking with a new automated framework as part of human-in-control-driven quality assurance work and questions of integration into an existing forensic process model.</p> <p> </p> <p class="presentation_time" style="text-align:left;">4:20<a name="MWSF-380"></a><span style="float: right;">MWSF-380</span> <br> <span class="presentation_title" final_id="MWSF-380" onclick="toggle_me()" style="cursor: pointer;">Detecting GAN-generated synthetic images using semantic inconsistencies, </span><span class="author_string" final_id="MWSF-380" onclick="toggle_me()" style="cursor: pointer;">Danial Samadi Vahdati and </span><span class="author_string" final_id="MWSF-380" onclick="toggle_me()" style="cursor: pointer;">Matthew C. Stamm</span><span class="author_string" final_id="MWSF-380" onclick="toggle_me()" style="cursor: pointer;">, Drexel University (United States)</span><span class="abstract_link" final_id="MWSF-380" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-380" id="abstract-MWSF-380" onclick="toggle_me()" style="display:none; cursor:pointer;">In the past several years, generative adversarial networks have emerged that are capable of creating realistic synthetic images of human faces. Because these images can be used for malicious purposes, researchers have begun to develop techniques to synthetic images. Currently, the majority of existing techniques operate by searching for statistical traces introduced when an image is synthesized by a GAN. An alternative approach that has received comparatively less research involves using semantic inconsistencies detect synthetic images. While GAN-generated synthetic images appear visually realistic at first glance, they often contain subtle semantic inconsistencies such as inconsistent eye highlights, misaligned teeth, unrealistic hair textures, etc. In this paper, we propose a new approach to detect GAN-generated images of human faces by searching for semantic inconsistencies in multiple different facial features such as the eyes, mouth, and hair. Synthetic image detection decisions are made by fusing the outputs of these facial-feature-level detectors. Through a series of experiments, we demonstrate that this approach can yield strong synthetic image detection performance. Furthermore, we experimentally demonstrate that our approach is less susceptible to performance degradations caused by post-processing than CNN-based detectors utilize statistical traces.</p> <p> </p> <p class="presentation_time" style="text-align:left;">4:45<a name="MWSF-381"></a><span style="float: right;">MWSF-381</span> <br> <span class="presentation_title" final_id="MWSF-381" onclick="toggle_me()" style="cursor: pointer;">Deepfake detection assisted by background matching, </span><span class="author_string" final_id="MWSF-381" onclick="toggle_me()" style="cursor: pointer;">Martin Steinebach<sup>1</sup>, </span><span class="author_string" final_id="MWSF-381" onclick="toggle_me()" style="cursor: pointer;">Stephanie Blümer<sup>2</sup>, </span><span class="author_string" final_id="MWSF-381" onclick="toggle_me()" style="cursor: pointer;">Niklas Bunzel<sup>1</sup>, and </span><span class="author_string" final_id="MWSF-381" onclick="toggle_me()" style="cursor: pointer;">Raphael A. Frick<sup>1</sup></span><span class="author_string" final_id="MWSF-381" onclick="toggle_me()" style="cursor: pointer;">; <sup>1</sup>Fraunhofer Institute for Secure Information Technology and <sup>2</sup>TU Darmstadt (Germany)</span><span class="abstract_link" final_id="MWSF-381" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-381" id="abstract-MWSF-381" onclick="toggle_me()" style="display:none; cursor:pointer;">To decide whether a video is a deep fake, we combine a method for finding potential sources (in the sense of inverse image searching) of the video and existing deepfake detection methods. We first find a second video (if available) that is nearly identical to the video under examination and only differs in the face region. Then we run a deep fake detection on both versions of the video. The video with the higher likelihood of being a deep fake is then identified as the deep fake. Thereby we circumvent the usual challenge of defining a suitable threshold for deep fakes in the detection process.</p> <p> </p> <p class="presentation_time" style="text-align:left;">5:10<br> <span class="presentation_title">Concluding Remarks</span> </p> <br> <br> <p class="event_time">5:30 – 7:00 PM EI 2023 Symposium Demonstration Session (in the Cyril Magnin Foyer)</p> <p class="date">Wednesday 18 January 2023</p> <p class="event_time">10:00 AM – 3:30 PM Industry Exhibition - Wednesday (in the Cyril Magnin Foyer)</p> <p class="event_time">10:20 – 10:50 AM Coffee Break</p> <p class="event_time">12:30 – 2:00 PM Lunch</p> <div class="pinkcallout"> <p class="session_title">Wednesday 18 January PLENARY: Bringing Vision Science to Electronic Imaging: The Pyramid of Visibility</p> <span class="chair">Session Chair: Andreas Savakis, Rochester Institute of Technology (United States)<br> </span> <span class="session_time">2:00 PM – 3:00 PM</span> <br> <span class="room">Cyril Magnin I/II/III<br> </span> <span></span> <p class="session_notes">Electronic imaging depends fundamentally on the capabilities and limitations of human vision. The challenge for the vision scientist is to describe these limitations to the engineer in a comprehensive, computable, and elegant formulation. Primary among these limitations are visibility of variations in light intensity over space and time, of variations in color over space and time, and of all of these patterns with position in the visual field. Lastly, we must describe how all these sensitivities vary with adapting light level. We have recently developed a structural description of human visual sensitivity that we call the Pyramid of Visibility, that accomplishes this synthesis. This talk shows how this structure accommodates all the dimensions described above, and how it can be used to solve a wide variety of problems in display engineering.</p> <br> <span></span> <p class="session_notes"> </p> <span class="author_string"><strong>Andrew B. Watson, </strong>chief vision scientist, Apple Inc. (United States)<span class="author_string"></span></span> <p> </p> <span></span> <p class="session_notes">Andrew Watson is Chief Vision Scientist at Apple, where he leads the application of vision science to technologies, applications, and displays. His research focuses on computational models of early vision. He is the author of more than 100 scientific papers and 8 patents. He has 21,180 citations and an h-index of 63. Watson founded the Journal of Vision, and served as editor-in-chief 2001-2013 and 2018-2022. Watson has received numerous awards including the Presidential Rank Award from the President of the United States.</p> </div> <br> <p class="event_time">3:00 – 3:30 PM Coffee Break</p> <p class="session_title">Media Watermarking, Security, and Forensics 2023 Interactive (Poster) Paper Session </p> <span class="session_time">5:30 – 7:00 PM</span> <br> <span class="room">Cyril Magnin Foyer </span> <br> <span></span> <p class="session_notes">The following work will be presented at the EI 2023 Symposium Interactive (Poster) Paper Session.</p> <br> <p class="presentation_time" style="text-align:left;"> <a name="MWSF-382"></a><span style="float: right;">MWSF-382</span> <br> <span class="presentation_title" final_id="MWSF-382" onclick="toggle_me()" style="cursor: pointer;">Making digital cameras less attractive targets for theft, </span><span class="author_string" final_id="MWSF-382" onclick="toggle_me()" style="cursor: pointer;">Henry G. Dietz and </span><span class="author_string" final_id="MWSF-382" onclick="toggle_me()" style="cursor: pointer;">Tofunmi Oyetan</span><span class="author_string" final_id="MWSF-382" onclick="toggle_me()" style="cursor: pointer;">, University of Kentucky (United States)</span><span class="abstract_link" final_id="MWSF-382" onclick="toggle_me()"> [view abstract] </span></p> <p class="abstract" final_id="MWSF-382" id="abstract-MWSF-382" onclick="toggle_me()" style="display:none; cursor:pointer;">Cameras are easy targets for theft. They are expensive, small, usually carried in the open, and not easily identifiable when stolen. Unlike cell phones, cameras typically don’t have passwords or other login procedures, so the full functionality is generally available to anyone with physical access to the camera, and stolen cameras behave indistinguishably from ones operated by their legitimate owners. The current work examines various methods for making cameras less attractive targets for theft without significantly increasing either camera cost or the complexity of the user interface and interactions. 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var ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTermsProperties = new SimpleSearchFieldProperties(); ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTermsProperties.WatermarkClass = 'Watermarked'; ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTermsProperties.WatermarkText = 'Keyword search'; ctl01_ciNewUtilityNavigationCommon2_ctl08_SearchTermsProperties.SearchTarget = 'https://www.imaging.org/Search'; NavigationList_NavControlId = 'ctl01_ciPrimaryNavigation_NavControl_NavMenu';NavigationList_Init();PageNavR_NavMenuClientID = 'ctl01_ciPrimaryNavigation_NavControl_NavMenu';var __wpmExportWarning='This Web Part Page has been personalized. As a result, one or more Web Part properties may contain confidential information. Make sure the properties contain information that is safe for others to read. After exporting this Web Part, view properties in the Web Part description file (.WebPart) by using a text editor such as Microsoft Notepad.';var __wpmCloseProviderWarning='You are about to close this Web Part. It is currently providing data to other Web Parts, and these connections will be deleted if this Web Part is closed. To close this Web Part, click OK. To keep this Web Part, click Cancel.';var __wpmDeleteWarning='You are about to permanently delete this Web Part. Are you sure you want to do this? To delete this Web Part, click OK. To keep this Web Part, click Cancel.';__wpm = new WebPartManager(); __wpm.overlayContainerElement = document.getElementById('ctl01_TemplateBody_WebPartManager1___Drag'); __wpm.personalizationScopeShared = false; var zoneElement; var zoneObject; zoneElement = document.getElementById('ctl01_TemplateBody_ContentPage1_WebPartZone1_Page1');if (zoneElement != null) {zoneObject = __wpm.AddZone(zoneElement, 'ctl01$TemplateBody$ContentPage1$WebPartZone1_Page1', true, false, 'Blue'); zoneObject.AddWebPart(document.getElementById('WebPart_gwpciCornerArt'), document.getElementById('WebPartTitle_gwpciCornerArt'), false); zoneObject.AddWebPart(document.getElementById('WebPart_gwpciSponsors_15ca9bc83de04973b59f22f081b05c66'), document.getElementById('WebPartTitle_gwpciSponsors_15ca9bc83de04973b59f22f081b05c66'), false); }zoneElement = document.getElementById('ctl01_TemplateBody_ContentPage2_WebPartZone2_Page1');if (zoneElement != null) {zoneObject = __wpm.AddZone(zoneElement, 'ctl01$TemplateBody$ContentPage2$WebPartZone2_Page1', true, false, 'Blue'); zoneObject.AddWebPart(document.getElementById('WebPart_gwpciConfCCO'), document.getElementById('WebPartTitle_gwpciConfCCO'), false); }if(typeof(window['ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_jsmanager'])=='undefined') { window['ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_jsmanager']=new Asi_Web_iParts_ContentCollectionOrganizer_ContentCollectionOrganizerDisplay('ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_radPage', 'False'); }Sys.Application.add_init(function() { $create(Telerik.Web.UI.RadMenu, {"_childListElementCssClass":"rmRootGroup rmToggleHandles rmHorizontal","_skin":"NaturalHeritageSites","attributes":{"Translate":"Yes","PerspectiveId":"80409b89-ae6d-45a9-a9d4-96d522ff2047","NavigationArea":"1","MaxDataBindDepth":"3"},"autoScrollMinimumWidth":100,"clientStateFieldID":"ctl01_ciPrimaryNavigation_NavControl_NavMenu_ClientState","collapseAnimation":"{\"duration\":450}","defaultGroupSettings":"{\"flow\":0,\"expandDirection\":2,\"offsetX\":0}","expandAnimation":"{\"duration\":450}","itemData":[],"showToggleHandle":true}, {"itemClicking":PageNavR_OnClientItemClicking,"itemClosed":PageNavR_OnItemClosed,"itemOpened":PageNavR_OnItemOpened,"load":PageNavR_OnClientLoadHandler}, null, $get("ctl01_ciPrimaryNavigation_NavControl_NavMenu")); }); Sys.Application.add_init(function() { $create(Telerik.Web.UI.RadTabStrip, {"_autoPostBack":true,"_postBackOnClick":true,"_postBackReference":"__doPostBack(\u0027ctl01$TemplateBody$WebPartManager1$gwpciConfCCO$ciConfCCO$radTab_Top\u0027,\u0027arguments\u0027)","_scrollButtonsPosition":1,"_selectedIndex":1,"_skin":"MetroTouch","causesValidation":false,"clientStateFieldID":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_radTab_Top_ClientState","enableAriaSupport":true,"multiPageID":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_radPage","selectedIndexes":["1"],"tabData":[{"value":"1","_implPageViewID":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_Page_1","attributes":{"translate":"yes"}},{"value":"2","_implPageViewID":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_Page_2","attributes":{"translate":"yes"}},{"value":"3","_implPageViewID":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_Page_3","attributes":{"translate":"yes"}},{"value":"4","_implPageViewID":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_Page_4","attributes":{"translate":"yes"}}]}, null, null, $get("ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_radTab_Top")); }); Sys.Application.add_init(function() { $create(Telerik.Web.UI.RadMultiPage, {"clientStateFieldID":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_radPage_ClientState","pageViewData":[{"id":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_Page_1"},{"id":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_Page_2"},{"id":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_Page_3"},{"id":"ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_Page_4"}],"selectedIndex":1}, null, null, $get("ctl01_TemplateBody_WebPartManager1_gwpciConfCCO_ciConfCCO_radPage")); }); Sys.Application.add_init(function() { $create(Telerik.Web.UI.RadAjaxManager, {"_updatePanels":"","ajaxSettings":[],"clientEvents":{OnRequestStart:"",OnResponseEnd:""},"defaultLoadingPanelID":"AjaxStatusLoadingPanel","enableAJAX":true,"enableHistory":false,"links":[],"styles":[],"uniqueID":"ctl01$RadAjaxManager1","updatePanelsRenderMode":0}, null, null, $get("ctl01_RadAjaxManager1")); }); Sys.Application.add_init(function() { $create(Telerik.Web.UI.RadWindow, {"_dockMode":false,"behaviors":117,"clientStateFieldID":"ctl01_GenericWindow_ClientState","enableAriaSupport":true,"formID":"aspnetForm","height":"550px","iconUrl":"","localization":"{\"Close\":\"Close\",\"Maximize\":\"Maximize\",\"Minimize\":\"Minimize\",\"Reload\":\"Reload\",\"PinOn\":\"PinOn\",\"PinOff\":\"PinOff\",\"Restore\":\"Restore\",\"OK\":\"OK\",\"Cancel\":\"Cancel\",\"Yes\":\"Yes\",\"No\":\"No\"}","minimizeIconUrl":"","modal":true,"name":"GenericWindow","shortcuts":"[[\u0027close\u0027,\u0027Esc\u0027]]","showContentDuringLoad":false,"skin":"MetroTouch","visibleStatusbar":false,"width":"800px"}, null, null, $get("ctl01_GenericWindow")); }); Sys.Application.add_init(function() { $create(Telerik.Web.UI.RadWindow, {"_dockMode":false,"behaviors":117,"clientStateFieldID":"ctl01_ObjectBrowser_ClientState","enableAriaSupport":true,"formID":"aspnetForm","height":"550px","iconUrl":"","localization":"{\"Close\":\"Close\",\"Maximize\":\"Maximize\",\"Minimize\":\"Minimize\",\"Reload\":\"Reload\",\"PinOn\":\"PinOn\",\"PinOff\":\"PinOff\",\"Restore\":\"Restore\",\"OK\":\"OK\",\"Cancel\":\"Cancel\",\"Yes\":\"Yes\",\"No\":\"No\"}","minimizeIconUrl":"","modal":true,"name":"ObjectBrowser","shortcuts":"[[\u0027close\u0027,\u0027Esc\u0027]]","showContentDuringLoad":false,"skin":"MetroTouch","visibleStatusbar":false,"width":"760px"}, null, null, $get("ctl01_ObjectBrowser")); }); Sys.Application.add_init(function() { $create(Telerik.Web.UI.RadWindow, {"_dockMode":false,"behaviors":117,"clientStateFieldID":"ctl01_ObjectBrowserDialog_ClientState","enableAriaSupport":true,"formID":"aspnetForm","height":"400px","iconUrl":"","localization":"{\"Close\":\"Close\",\"Maximize\":\"Maximize\",\"Minimize\":\"Minimize\",\"Reload\":\"Reload\",\"PinOn\":\"PinOn\",\"PinOff\":\"PinOff\",\"Restore\":\"Restore\",\"OK\":\"OK\",\"Cancel\":\"Cancel\",\"Yes\":\"Yes\",\"No\":\"No\"}","minimizeIconUrl":"","modal":true,"name":"ObjectBrowserDialog","shortcuts":"[[\u0027close\u0027,\u0027Esc\u0027]]","showContentDuringLoad":false,"skin":"MetroTouch","visibleStatusbar":false,"width":"600px"}, null, null, $get("ctl01_ObjectBrowserDialog")); }); Sys.Application.add_init(function() { $create(Telerik.Web.UI.RadWindowManager, {"behaviors":117,"clientStateFieldID":"ctl01_WindowManager1_ClientState","enableAriaSupport":true,"formID":"aspnetForm","iconUrl":"","localization":"{\"Close\":\"Close\",\"Maximize\":\"Maximize\",\"Minimize\":\"Minimize\",\"Reload\":\"Reload\",\"PinOn\":\"PinOn\",\"PinOff\":\"PinOff\",\"Restore\":\"Restore\",\"OK\":\"OK\",\"Cancel\":\"Cancel\",\"Yes\":\"Yes\",\"No\":\"No\"}","minimizeIconUrl":"","name":"WindowManager1","shortcuts":"[[\u0027close\u0027,\u0027Esc\u0027]]","skin":"MetroTouch","windowControls":"['ctl01_GenericWindow','ctl01_ObjectBrowser','ctl01_ObjectBrowserDialog']"}, null, {"child":"ctl01_GenericWindow"}, $get("ctl01_WindowManager1")); }); //]]> </script> </form> <div id="fb-root"></div> </body> </html>