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Building a Glossary for Synthetic Media Transparency Methods - Partnership on AI
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<script type="text/javascript" src="https://unpkg.com/just-validate@latest/dist/just-validate.production.min.js?ver=6.6.1" id="just-validate-js"></script> <!-- Google Tag Manager for WordPress by gtm4wp.com --> <!-- GTM Container placement set to automatic --> <script data-cfasync="false" data-pagespeed-no-defer type="text/javascript"> var dataLayer_content = {"visitorLoginState":"logged-out","visitorType":"visitor-logged-out","pagePostType":"resource","pagePostType2":"single-resource","pagePostAuthor":"Neil Uhl","pagePostDate":"December 13, 2023","pagePostDateYear":2023,"pagePostDateMonth":12,"pagePostDateDay":13,"pagePostDateDayName":"Wednesday","pagePostDateHour":16,"pagePostDateMinute":39,"pagePostDateIso":"2023-12-13T16:39:56-05:00","pagePostDateUnix":1702485596,"pagePostTerms":{"resource-content-type":["Research and Publications"],"workstream_tax":["Synthetic and Manipulated Content"],"meta":{"full_text_search":"Building a Glossary for Synthetic Media Transparency Methods Part 1: Indirect Disclosure According to an Axios poll, a third of Americans say AI will reduce their trust in the results of the 2024 U.S. election. For Partnership on AI (PAI) and our community of organizations building, creating, and distributing synthetic media, this raises important questions: How can everyday citizens know if the media they see and hear has been created or modified by AI and, more importantly, is misleading? And what can organizations do to improve transparency? There is no perfect answer. However, those building, creating, and distributing synthetic media can act to support better explanations of how content has been created and edited. They have a responsibility, when appropriate, to make clear to audiences that content is AI-generated or AI-modified (and any uncertainty about such judgments). While many disparate efforts have emerged to help audiences navigate an increasingly synthetic information environment, largely by providing context about content, the community has not aligned on which combination of tactics to implement, when to share insights with audiences, and how they can evaluate their efficacy in supporting trustworthy content. The need for alignment is urgent. Policymakers—including the White House in its recent Executive Order—have begun to explore different techniques for technology, government, civil society, and media organizations, to provide audiences with greater insight into what they see and hear. To improve alignment and understanding of synthetic media transparency, PAI has developed a series of resources. Part 1 of the series, focused on indirect disclosure, includes: A community-driven glossary defining the common technical methods that can provide insight into whether media is synthetic or not—what we describe as indirect disclosure methods; Proposed questions for evaluating indirect disclosure methods; and Initial guidance on how policymakers and those building, creating, and distributing synthetic media should implement these techniques. The glossary below builds off of a seven-part workshop series with 18 organizations that are collaborators on PAI’s Responsible Practices for Synthetic Media. While the information in this document is provided by PAI and is not intended to reflect the view of any other organization, much of it has been informed through collaboration. Different organizations use different terms to refer to the class of methods described below. However, we encourage the use of the term indirect disclosure to center these methods in how they contribute to the goal of broader AI transparency. While the disclosure taking place will not always translate to transparency for audiences, it does reveal details about content’s AI-generation or AI-modification to entities involved in content development, creation, and distribution. Such information can be used to support and underscore broader societal transparency goals. We will explore direct disclosure in future blog posts. Graphic Notes: Synthetic Media Detection is set apart in the graphic since it is slightly different from other technical methods for synthetic media transparency; such detection derives signals from media and does not require any action taken to implant a signal in content, like indirect disclosure methods do. See below for more detail on the proactive and derived dynamics of these methods. Glossary We include methods in the glossary based on a combination of factors, including whether or not they are in use by those developing, creating, and distributing synthetic media, as well as input from our 18 Framework collaborators. Inclusion in the glossary does not equate to recommended use—rather, the Initial Recommendation section below offers initial guidance on the optimal combination of indirect disclosure methods, which we will further develop in future blogs. We also include key terms in the glossary to help support understanding of the methods in the graphic above. Synthetic Media (or “Generative Media”) Visual, auditory, or multimodal content that has been generated or modified (commonly via artificial intelligence). Such outputs are often highly realistic, would not be identifiable as synthetic to the average person, and may simulate artifacts, persons, or events. Examples Ukraine: Deepfake of President Zelensky Asking Ukrainian Forces to Surrender Argentina: AI-Generated Image Campaign Showing What Abducted Children May Look Like as Adults Pakistan: Deepfake of Former Prime Minister Khan Addressing an Online Elections Rally Synthetic Media Transparency Methods The umbrella term used to describe signals for conveying whether a piece of media is AI-generated or AI-modified. Such signals can either be indirect (not user facing) or direct (user facing). Indirect disclosure signals can support understanding of whether content has been AI-generated or AI-modified and, when appropriate, guide development of direct disclosures to audiences and end-users. Examples Indirect disclosures, direct disclosures, synthetic media detection Indirect Disclosure A signal for conveying whether a piece of media is AI-generated or AI-modified, based on information about a piece of content’s origin and\/or its evolution; is not user facing. The “disclosure” that takes place is typically to entities involved in content development, creation, and distribution—but it can be used to inform direct, or audience\/user-facing, disclosure. Examples Watermarks, fingerprints, metadata Proactive Methods At generation Post-generation Synthetic media transparency methods that rely upon an actor purposefully applying a signal that can then be identified by a third-party who is able to detect or interpret the signal. Notably, in an adversarial setting, bad actors will not leverage such techniques and may attempt to alter existing signals. Can be classified further into two categories: At generation Signal is applied automatically by a media generation model at the moment of creation. Post generation Signal is applied after creation. Examples Watermarks, fingerprints, metadata Derived Methods Synthetic media transparency methods that determine the origin or evolution of media based on signals that do not rely upon a disclosure signal being applied by an actor. Examples Synthetic media detection Direct Disclosure A signal for conveying to users whether a piece of media is AI-generated or AI-modified; often informed by indirect disclosures. Examples Labels, content overlays Watermarking Invisible Visible The proactive (at, or post, generation) insertion of modifications into a piece of content that can help support interpretations of how the content was generated and\/or edited. Can come in two forms: Invisible Modifications made to a piece of content that are imperceptible to the human eye or ear. Can only be identified by a watermark detector (distinct from Synthetic Media Detection). Visible Modifications made to a piece of synthetic content that are detectable to the human eye or ear and do not require the use of a detector to interpret them. Examples Google’s SynthID (invisible), Meta AI's Imagine (visible) Watermark Key\/Detector A digital tool, similar to a password, that is required for detecting an invisible\/ hidden watermark embedded in a piece of content. Can be shared broadly (open) or be restricted to select players (closed), or in between. This choice can affect the technical robustness of a watermark and its societal impact. Fingerprinting Cryptographic hashing Perceptual hashing The proactive (at, or post, generation) process by which a hash is generated for a piece of content for the purpose of identifying that content at a later date. Such hashes must be stored in a database in order to verify future content against the original. Unlike watermarking, this hash is not embedded in the content file itself. Also known as “hashing and matching” or “hashing and logging.” Can come in two forms: Cryptographic hashing An exact-match form of hashing where the hash for a piece of synthetic content will not match if the content has been modified in any way. Perceptual hashing A probabilistic-match form of hashing where the hash for a piece of synthetic content is resilient to minor perturbations (i.e., will still match with minor changes). Example YouTube's Content ID Metadata Signed Unsigned Information about the origin, structure, and\/or editing history of a piece of content that is proactively attached to the content itself. Signed Metadata Information that is proactively attached to the content itself and stored using secure encryption; a trusted\/validated signer certificate is added post generation. State of the art methods leverage cryptographic signatures. Unsigned Metadata Information that is proactively attached to the content itself at generation but is not stored with secure encryption or validated with a trusted signer certificate, and potentially can be changed imperceptibly (weakening robustness). Note In policy and public discourse, metadata has sometimes been described as “media provenance” despite the fact that provenance can be a broader term describing all the methods under the umbrella of indirect disclosure. For example, several PAI Partners refer to indirect disclosure methods as provenance methods. It is our hope that the terminology expressed in this glossary helps align the field on nomenclature. Examples C2PA Standard (signed), IPTC Standard (unsigned) Synthetic Media Detection Methods that rely on detecting unintentionally added patterns\/forensic cues differentiating synthetic media from non-synthetic media to determine the likelihood that a piece of content was AI-generated or AI-modified; such methods do not rely on theproactive addition of artifacts such as watermarks in content. Synthetic media detection is a derived transparency method. Examples Intel’s FakeCatcher, Google Jigsaw’s Assembler (no longer active) Detector Access Open Closed Whether for watermark detection or synthetic media detection, systems identifying synthetic media can either be shared broadly or restricted in their access, or somewhere in between. Closed Detectors Only available to a select number of organizations, minimizing the risk of adversarial exploitation at the expense of accessibility. Open Detectors Widely available, maximizing accessibility at the expense of increased risk of adversarial exploitation. Note Detector access does not necessarily have to be a binary choice between open and closed. PAI has conducted much work on the tradeoffs between open and closed access; see here, here, and here, all incorporating recommendations for “goldilocks” exposure between open and closed. 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href="https://www.facebook.com/sharer/sharer.php?u=https://partnershiponai.org/resource/glossary-for-synthetic-media-transparency-methods-part-1/"><i class="fab fa-facebook-f"></i></a> </li> <li> <a target="_blank" class="btn" href="https://twitter.com/intent/tweet?url=https://partnershiponai.org/resource/glossary-for-synthetic-media-transparency-methods-part-1/&text=Building a Glossary for Synthetic Media Transparency Methods&via="><i class="fab fa-twitter"></i></a> </li> <li> <a target="_blank" class="btn" href="https://www.linkedin.com/shareArticle?mini=true&url=https://partnershiponai.org/resource/glossary-for-synthetic-media-transparency-methods-part-1/&title=Building a Glossary for Synthetic Media Transparency Methods"><i class="fab fa-linkedin-in"></i></a> </li> </ul> </div> <div class="post-title"> <a href="https://partnershiponai.org/resources/"> Our Resources </a> <h5> / </h5> <h5>Research and Publications</h5> </div> <div class="program-name"> <a href="https://partnershiponai.org/workstream/synthetic-and-manipulated-content/"><span class="content_type sm-text">Synthetic and Manipulated Content</span></a> </div> <div class="post-subtitle"> <h1 class="detail">Building a Glossary for Synthetic Media Transparency Methods </h1> <h3 class="post-subtitle">Part 1: Indirect Disclosure</h3> </div> <div class="post-author"> <div class="author"> <p class="byline author vcard"> </p> <time class="updated" datetime="2023-12-13T21:39:56+00:00">December 13, 2023</time> </div> </div> <div class="hero-image"> <img decoding="async" src="https://partnershiponai.org/wp-content/uploads/2024/11/fingerprints_green.png" alt="$hero_image['alt']" loading="lazy"> </div> </div> </div> <div class="page-content-container"> <div class="content"> <main class="main content-wrapper-wp-frontend"> <!-- <div class="empty-content"> <div class="empty-content-inner"> <div class="head"> Content Wrapper </div> <div class="title"> Click here to start editing </div> </div> </div> --> <!-- Content Row --> <div class="page-row row-transparent page-row-wp-frontend" > <div class="container"> <!-- <div class="empty-content"> <div class="empty-content-inner"> <div class="head"> Content Row </div> <div class="title"> Click here to start editing </div> </div> </div> --> <div class="module wysiwyg-module "> <div class="container "> <div class="wysiwyg-content col-lg-12 col-md-12 "> <p>According to an <a href="https://www.axios.com/2023/09/11/poll-ai-elections-axios-morning-consult">Axios poll</a>, a third of Americans say AI will reduce their trust in the results of the 2024 U.S. election. For Partnership on AI (PAI) and our community of organizations building, creating, and distributing synthetic media, this raises important questions: How can everyday citizens know if the media they see and hear has been created or modified by AI and, more importantly, is misleading? And what can organizations do to improve transparency?</p> <p><strong>There is no perfect answer.</strong> However, those building, creating, and distributing synthetic media can act to support better explanations of how content has been created and edited. They have a responsibility, when appropriate, to make clear to audiences that content is AI-generated or AI-modified (and any uncertainty about such judgments).</p> <p>While many disparate efforts have emerged to help audiences navigate an increasingly synthetic information environment, largely by providing context about content, <strong>the community has not aligned </strong>on which combination of tactics to implement, when to share insights with audiences, and how they can evaluate their efficacy in supporting trustworthy content.</p> <p><strong>The need for alignment is urgent.</strong> Policymakers—including the White House in its recent <a href="https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/">Executive Order</a>—have begun to explore different techniques for technology, government, civil society, and media organizations, to provide audiences with greater insight into what they see and hear.</p> <p>To improve alignment and understanding of synthetic media transparency, PAI has developed a series of resources. Part 1 of the series, focused on <a class="glossarylink" href="#Indirect_Disclosure">indirect disclosure</a>, includes:</p> <ol> <li>A community-driven <a href="#Glossary">glossary</a> defining the common technical methods that can provide insight into whether media is synthetic or not—what we describe as <a class="glossarylink" href="#Indirect_Disclosure">indirect disclosure</a> methods;</li> <li>Proposed questions for evaluating <a class="glossarylink" href="#Indirect_Disclosure">indirect disclosure</a> methods; and</li> <li>Initial guidance on how policymakers and those building, creating, and distributing synthetic media should implement these techniques.</li> </ol> <p>The <a href="#Glossary">glossary</a> below builds off of a seven-part workshop series with 18 organizations that are collaborators on PAI’s <a href="https://syntheticmedia.partnershiponai.org/">Responsible Practices for Synthetic Media</a>. While the information in this document is provided by PAI and is not intended to reflect the view of any other organization, much of it has been informed through collaboration.</p> <p>Different organizations use different terms to refer to the class of methods described below. However, we encourage the use of the term <a class="glossarylink" href="#Indirect_Disclosure">indirect disclosure</a> to center these methods in how they contribute to the goal of broader AI transparency. While the disclosure taking place will not always translate to transparency for audiences, it <em>does</em> reveal details about content’s AI-generation or AI-modification to entities involved in content development, creation, and distribution. Such information <em>can</em> be used to support and underscore broader societal transparency goals. We will explore <a class="glossarylink" href="#Direct_Disclosure">direct disclosure</a> in <a href="#Future_Work">future blog posts</a>.</p> </div> </div> </div> <div class="module wysiwyg-module "> <div class="container "> <div class="wysiwyg-content col-lg-12 col-md-12 "> <p><a href="https://partnershiponai.org/wp-content/uploads/2023/12/synth-media_taxonomy.svg"><img decoding="async" class="alignnone wp-image-10264" src="https://partnershiponai.org/wp-content/uploads/2023/12/synth-media_taxonomy.svg" alt="" width="800" /></a></p> <div class="footnote">Graphic Notes: <a class="glossarylink" href="#Detection">Synthetic Media Detection</a> is set apart in the graphic since it is slightly different from other technical methods for synthetic media transparency; such detection <em>derives</em> signals from media and does not require any action taken to implant a signal in content, like indirect disclosure methods do. See below for more detail on the <a class="glossarylink" href="#Proactive">proactive</a> and <a class="glossarylink" href="#Derived">derived</a> dynamics of these methods.</div> </div> </div> </div> <div class="module wysiwyg-module "> <div class="container "> <div class="wysiwyg-content col-lg-12 col-md-12 "> <div id="Glossary" data-anchor="Glossary"> <h3>Glossary</h3> <p>We include methods in the glossary based on a combination of factors, including whether or not they are in use by those developing, creating, and distributing synthetic media, as well as input from our 18 Framework collaborators. Inclusion in the glossary does not equate to recommended use—rather, the <a href="#Recommendation">Initial Recommendation</a> section below offers initial guidance on the optimal combination of <a class="glossarylink" href="#Indirect_Disclosure">indirect disclosure</a> methods, which we will further develop in future blogs. We also include key terms in the glossary to help support understanding of the methods in the graphic above.</p> <div class="row linetop"> <div id="Synthetic_Media" data-anchor="Synthetic_Media"></div> <div class="col-sm-12 col-md-4"><strong>Synthetic Media (or “Generative Media”)</strong></div> <div class="col-sm-12 col-md-8"> <p>Visual, auditory, or multimodal content that has been generated or modified (commonly via artificial intelligence). Such outputs are often highly realistic, would not be identifiable as synthetic to the average person, and may simulate artifacts, persons, or events.</p> <div class="h8">Examples</div> <ul> <li>Ukraine: <a href="https://www.npr.org/2022/03/16/1087062648/deepfake-video-zelenskyy-experts-war-manipulation-ukraine-russia">Deepfake of President Zelensky Asking Ukrainian Forces to Surrender</a></li> <li>Argentina: <a href="https://restofworld.org/2023/argentina-stolen-babies-ai-art/">AI-Generated Image Campaign Showing What Abducted Children May Look Like as Adults</a></li> <li>Pakistan: <a href="https://www.forbes.com/sites/siladityaray/2023/12/18/imran-khan-pakistans-jailed-ex-leader-uses-ai-deepfake-to-address-online-election-rally/?sh=379c1f115903">Deepfake of Former Prime Minister Khan Addressing an Online Elections Rally</a></li> </ul> </div> </div> <div class="row linetop"> <div id="Transparency_Methods" data-anchor="Transparency_Methods"></div> <div class="col-sm-12 col-md-4"><strong>Synthetic Media Transparency Methods</strong></div> <div class="col-sm-12 col-md-8"> <p>The umbrella term used to describe signals for conveying whether a piece of media is AI-generated or AI-modified. Such signals can either be <a class="glossarylink" href="#Indirect_Disclosure">indirect</a> (not user facing) or <a class="glossarylink" href="#Direct_Disclosure">direct</a> (user facing). <a class="glossarylink" href="#Indirect_Disclosure">Indirect disclosure</a> signals can support understanding of whether content has been AI-generated or AI-modified and, when appropriate, guide development of <a class="glossarylink" href="#Direct_Disclosure">direct disclosures</a> to audiences and end-users.</p> <div class="h8">Examples</div> <p><a class="glossarylink" href="#Indirect_Disclosure">Indirect disclosures</a>, <a class="glossarylink" href="#Direct_Disclosure">direct disclosures</a>, <a class="glossarylink" href="#Detection">synthetic media detection</a></p> </div> </div> <div class="row linetop"> <div id="Indirect_Disclosure" data-anchor="Indirect_Disclosure"></div> <div class="col-sm-12 col-md-4"><strong>Indirect Disclosure</strong></div> <div class="col-sm-12 col-md-8"> <p>A signal for conveying whether a piece of media is AI-generated or AI-modified, based on information about a piece of content’s origin and/or its evolution; is not user facing. The “disclosure” that takes place is typically to entities involved in content development, creation, and distribution—but it can be used to inform direct, or audience/user-facing, disclosure.</p> <div class="h8">Examples</div> <p><a class="glossarylink" href="#Watermarking">Watermarks</a>, <a class="glossarylink" href="#Fingerprinting">fingerprints</a>, <a class="glossarylink" href="#Metadata">metadata</a></p> </div> </div> <div class="row linetop"> <div id="Proactive" data-anchor="Proactive"></div> <div class="col-sm-12 col-md-4"> <p><strong>Proactive Methods</strong></p> <ul> <li>At generation</li> <li>Post-generation</li> </ul> </div> <div class="col-sm-12 col-md-8"> <p><a class="glossarylink" href="#Transparency_Methods">Synthetic media transparency methods</a> that rely upon an actor purposefully applying a signal that can then be identified by a third-party who is able to detect or interpret the signal. Notably, in an adversarial setting, bad actors will not leverage such techniques and may attempt to alter existing signals.</p> <p>Can be classified further into two categories:</p> <div class="h8">At generation</div> <p>Signal is applied automatically by a media generation model at the moment of creation.</p> <div class="h8">Post generation</div> <p>Signal is applied after creation.</p> <div class="h8">Examples</div> <p><a class="glossarylink" href="#Watermarking">Watermarks</a>, <a class="glossarylink" href="#Fingerprinting">fingerprints</a>, <a class="glossarylink" href="#Metadata">metadata</a></p> </div> </div> <div class="row linetop"> <div id="Derived" data-anchor="Derived"></div> <div class="col-sm-12 col-md-4"><strong>Derived Methods</strong></div> <div class="col-sm-12 col-md-8"> <p><a class="glossarylink" href="#Transparency_Methods">Synthetic media transparency methods</a> that determine the origin or evolution of media based on signals that do not rely upon a disclosure signal being applied by an actor.</p> <div class="h8">Examples</div> <p><a class="glossarylink" href="#Detection">Synthetic media detection</a></p> </div> </div> <div class="row linetop"> <div id="Direct_Disclosure" data-anchor="Direct_Disclosure"></div> <div class="col-sm-12 col-md-4"><strong>Direct Disclosure</strong></div> <div class="col-sm-12 col-md-8"> <p>A signal for conveying <em>to users</em> whether a piece of media is AI-generated or AI-modified; often informed by <a class="glossarylink" href="#Indirect_Disclosure">indirect disclosures</a>.</p> <div class="h8">Examples</div> <p>Labels, content overlays</p> </div> </div> <div class="row linetop"> <div id="Watermarking" data-anchor="Watermarking"></div> <div class="col-sm-12 col-md-4"> <p><strong><img decoding="async" class="alignnone wp-image-10221" src="https://partnershiponai.org/wp-content/uploads/2023/12/synth-media-_watermark.svg" alt="" width="35" /> Watermarking </strong></p> <ul> <li>Invisible</li> <li>Visible</li> </ul> </div> <div class="col-sm-12 col-md-8"> <p>The <a class="glossarylink" href="#Proactive">proactive</a> (at, or post, generation) insertion of modifications into a piece of content that can help support interpretations of how the content was generated and/or edited. Can come in two forms:</p> <div class="h8">Invisible</div> <p>Modifications made to a piece of content that are imperceptible to the human eye or ear. Can only be identified by a <a class="glossarylink" href="#Watermark_Key">watermark detector</a> (distinct from <a class="glossarylink" href="#Detection">Synthetic Media Detection</a>).</p> <div class="h8">Visible</div> <p>Modifications made to a piece of synthetic content that are detectable to the human eye or ear and do not require the use of a <a class="glossarylink" href="#Watermark_Key">detector</a> to interpret them.</p> <div class="h8">Examples</div> <p>Google’s <a href="https://deepmind.google/technologies/synthid/">SynthID</a> (invisible), Meta AI’s <a href="https://about.fb.com/news/2023/09/building-generative-ai-features-responsibly/">Imagine</a> (visible)</p> </div> </div> <div class="row linetop"> <div id="Watermark_Key" data-anchor="Watermark_Key"></div> <div class="col-sm-12 col-md-4"><strong>Watermark Key/Detector</strong></div> <div class="col-sm-12 col-md-8">A digital tool, similar to a password, that is required for detecting an invisible/ hidden <a class="glossarylink" href="#Watermarking">watermark</a> embedded in a piece of content. Can be shared broadly (open) or be restricted to select players (closed), or in between. This choice can affect the technical robustness of a watermark and its societal impact.</div> </div> <div class="row linetop"> <div id="Fingerprinting" data-anchor="Fingerprinting"></div> <div class="col-sm-12 col-md-4"> <p><strong><img decoding="async" class="alignnone wp-image-10219" src="https://partnershiponai.org/wp-content/uploads/2023/12/synth-media-_fingerprint.svg" alt="" width="35" /> Fingerprinting </strong></p> <ul> <li>Cryptographic hashing</li> <li>Perceptual hashing</li> </ul> </div> <div class="col-sm-12 col-md-8"> <p>The <a class="glossarylink" href="#Proactive">proactive</a> (at, or post, generation) process by which a hash is generated for a piece of content for the purpose of identifying that content at a later date. Such hashes must be stored in a database in order to verify future content against the original. Unlike <a class="glossarylink" href="#Watermarking">watermarking</a>, this hash is not embedded in the content file itself. Also known as “hashing and matching” or “hashing and logging.” Can come in two forms:</p> <div class="h8">Cryptographic hashing</div> <p>An exact-match form of hashing where the hash for a piece of synthetic content will not match if the content has been modified in any way.</p> <div class="h8">Perceptual hashing</div> <p>A probabilistic-match form of hashing where the hash for a piece of synthetic content is resilient to minor perturbations (i.e., will still match with minor changes).</p> <div class="h8">Example</div> <p>YouTube’s <a href="https://support.google.com/youtube/answer/2797370?hl=en">Content ID</a></p> </div> </div> <div class="row linetop"> <div id="Metadata" data-anchor="Metadata"></div> <div class="col-sm-12 col-md-4"> <p><strong><img decoding="async" class="alignnone wp-image-10220" src="https://partnershiponai.org/wp-content/uploads/2023/12/synth-media-_metadata.svg" alt="" width="35" /> Metadata </strong></p> <ul> <li>Signed</li> <li>Unsigned</li> </ul> </div> <div class="col-sm-12 col-md-8"> <p>Information about the origin, structure, and/or editing history of a piece of content that is <a class="glossarylink" href="#Proactive">proactively</a> attached to the content itself.</p> <div class="h8">Signed Metadata</div> <p>Information that is <a class="glossarylink" href="#Proactive">proactively</a> attached to the content itself and stored using secure encryption; a trusted/validated signer certificate is added post generation. State of the art methods leverage cryptographic signatures.</p> <div class="h8">Unsigned Metadata</div> <p>Information that is <a class="glossarylink" href="#Proactive">proactively</a> attached to the content itself at generation but is <em>not</em> stored with secure encryption or validated with a trusted signer certificate, and potentially can be changed imperceptibly (weakening robustness).</p> <div class="h8">Note</div> <p>In policy and public discourse, metadata has sometimes been described as “media provenance” despite the fact that provenance can be a broader term describing <em>all</em> the methods under the umbrella of <a class="glossarylink" href="#Indirect_Disclosure">indirect disclosure</a>. For example, several PAI Partners refer to indirect disclosure methods as provenance methods. It is our hope that the terminology expressed in this glossary helps align the field on nomenclature.</p> <div class="h8">Examples</div> <p><a href="https://c2pa.org/specifications/specifications/1.3/specs/C2PA_Specification.html#_digital_signatures">C2PA Standard</a> (signed), <a href="https://iptc.org/standards/photo-metadata/iptc-standard/">IPTC Standard</a> (unsigned)</p> </div> </div> <div class="row linetop"> <div id="Detection" data-anchor="Detection"></div> <div class="col-sm-12 col-md-4"><strong>Synthetic Media Detection</strong></div> <div class="col-sm-12 col-md-8"> <p>Methods that rely on detecting unintentionally added patterns/forensic cues differentiating synthetic media from non-synthetic media to determine the likelihood that a piece of content was AI-generated or AI-modified; such methods do not rely on the<a class="glossarylink" href="#Proactive">proactive</a> addition of artifacts such as <a class="glossarylink" href="#Watermarking">watermarks</a> in content. Synthetic media detection is a <a class="glossarylink" href="#Derived">derived</a> transparency method.</p> <div class="h8">Examples</div> <p>Intel’s <a href="https://www.intel.com/content/www/us/en/newsroom/news/intel-introduces-real-time-deepfake-detector.html#gs.1akesv">FakeCatcher</a>, Google Jigsaw’s <a href="https://projectassembler.org/">Assembler</a> (no longer active)</p> </div> <div class="row linetop"> <div id="Detector" data-anchor="Detector"></div> <div class="col-sm-12 col-md-4"> <p><strong>Detector Access</strong></p> <ul> <li>Open</li> <li>Closed</li> </ul> </div> <div class="col-sm-12 col-md-8"> <p>Whether for <a class="glossarylink" href="#Watermark_Key">watermark detection</a> or <a class="glossarylink" href="#Detection">synthetic media detection</a>, systems identifying synthetic media can either be shared broadly or restricted in their access, or somewhere in between.</p> <div class="h8">Closed Detectors</div> <p>Only available to a select number of organizations, minimizing the risk of adversarial exploitation at the expense of accessibility.</p> <div class="h8">Open Detectors</div> <p>Widely available, maximizing accessibility at the expense of increased risk of adversarial exploitation.</p> <div class="h8">Note</div> <p>Detector access does not necessarily have to be a binary choice between open and closed. PAI has conducted much work on the tradeoffs between open and closed access; see <a href="https://arxiv.org/pdf/2102.06109.pdf">here</a>, <a href="https://techpolicy.press/governing-access-to-synthetic-media-detection-technology/">here</a>, and <a href="https://partnershiponai.org/modeldeployment/">here</a>, all incorporating recommendations for “goldilocks” exposure between open and closed.</p> </div> </div> <div class="row linetop"></div> </div> <p> </p> </div> </div> </div> </div> </div> </div> </main> </div> </div> <div class="page-row blog-back-btn-section"> <div class="container"> <div class="module wysiwyg-module "> <div class="container "> <div class="wysiwyg-content col-lg-12 col-md-12 "> <a href="https://partnershiponai.org/resources/" class="btn mt-0 margin-bottom-5 blue small"> Back to All Resources</a> </div> </div> </div> </div> </div> </div> <footer> </footer> <section id="comments" class="comments"> </section> </article> </main> </div> </div> <footer class="site-footer content-info"> <div class="left-dot dot dot-sm"><div class="left-inner-dot dot dot-xs dot-coral"></div></div> <!-- <div class="right-dot dot dot-sm dot-coral"></div> --> <div class="footer-top"> <div class="footer-top-inner container"> <div class="footer-top-wrap"> <div class="footer-social-links"> <ul class="social-links"> <li><a href="https://twitter.com/partnershipai?lang=en" target="_blank" class="btn"><i class="fab fa-twitter"></i></a></li> <li><a href="https://www.facebook.com/partnershipai/" target="_blank" class="btn"><i class="fab fa-facebook-f"></i></a></li> <li><a href="https://www.linkedin.com/company/partnershipai" target="_blank" class="btn"><i class="fab fa-linkedin-in"></i></a></li> </ul> </div> <div class="footer-menu"> <nav class="nav footer-nav"> <div class="menu-footer-menu-container"><ul id="menu-footer-menu-2" class="nav"><li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-2159"><a href="https://new.partnershiponai.org/work">OUR WORK</a></li> <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-2160"><a href="https://new.partnershiponai.org/resources/">RESOURCES</a></li> <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-5232"><a href="https://partnershiponai.org/events/">EVENTS</a></li> <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-2161"><a href="/about">ABOUT US</a></li> <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-2162"><a href="/partners">PARTNERS</a></li> <li class="footer-only menu-item menu-item-type-custom menu-item-object-custom menu-item-10338"><a href="/donate">SUPPORT US</a></li> </ul></div> </nav> </div> </div> </div> </div> <div class="footer-bottom"> <div class="container footer-header-container"> <div class="copyright"><p>© 2024 Partnership on AI | All Rights Reserved</p></div> <div class="footer-bottom-link"> <ul> <li> <a href="/transparency-governance">Transparency and Governance</a> </li> <li> <a href="https://new.partnershiponai.org/privacy-policy/">Privacy Policy</a> </li> </ul> </div> </div> </div> </footer> <div id="sizer"></div> <script type="text/javascript" src="https://partnershiponai.org/wp-includes/js/dist/hooks.min.js?ver=2810c76e705dd1a53b18" id="wp-hooks-js"></script> <script type="text/javascript" src="https://partnershiponai.org/wp-includes/js/dist/i18n.min.js?ver=5e580eb46a90c2b997e6" id="wp-i18n-js"></script> <script type="text/javascript" id="wp-i18n-js-after"> /* <![CDATA[ */ wp.i18n.setLocaleData( { 'text direction\u0004ltr': [ 'ltr' ] } ); /* ]]> */ </script> <script type="text/javascript" src="https://partnershiponai.org/wp-content/plugins/contact-form-7/includes/swv/js/index.js?ver=5.9.8" id="swv-js"></script> <script type="text/javascript" id="contact-form-7-js-extra"> /* <![CDATA[ */ var wpcf7 = {"api":{"root":"https:\/\/partnershiponai.org\/wp-json\/","namespace":"contact-form-7\/v1"},"cached":"1"}; /* ]]> */ </script> <script type="text/javascript" src="https://partnershiponai.org/wp-content/plugins/contact-form-7/includes/js/index.js?ver=5.9.8" id="contact-form-7-js"></script> <script type="text/javascript" id="dlm-xhr-js-extra"> /* <![CDATA[ */ var dlmXHRtranslations = {"error":"An error occurred while trying to download the file. 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