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DOISerbia - Semantic feature-based test selection for deep neural networks: A frequency domain perspective - Jiang, Zhouxian; Li, Honghui; Tian, Xuetao; Wang, Rui
<!DOCTYPE html> <html> <head id="MasterHeader"><title> DOISerbia - Semantic feature-based test selection for deep neural networks: A frequency domain perspective - Jiang, Zhouxian; Li, Honghui; Tian, Xuetao; Wang, Rui </title><meta name="robots" content="index,follow" /> <!-- Google tag (gtag.js) --> <script async src="https://www.googletagmanager.com/gtag/js?id=G-NJRRP467ME"></script> <script> window.dataLayer = window.dataLayer || []; function gtag() { dataLayer.push(arguments); } gtag('js', new Date()); gtag('config', 'G-NJRRP467ME'); </script> <link href="App_Themes/KoBSON/main.css" type="text/css" rel="stylesheet" /><meta name="description" content="Jiang, Zhouxian; Li, Honghui; Tian, Xuetao; Wang, Rui - Semantic feature-based test selection for deep neural networks: A frequency domain perspective - Computer Science and Information Systems" /><meta property="og:title" content="DOI Serbia 膷asopisi" /><meta property="og:url" content="https://doiserbia.nb.rs/Article.aspx?ID=1820-02142400045J" /><meta name="citation_journal_title" content="Computer Science and Information Systems" /><meta name="citation_title" content="Semantic feature-based test selection for deep neural networks: A frequency domain perspective" /><meta name="citation_author" content="Jiang, Zhouxian" /><meta name="citation_author" content="Li, Honghui" /><meta name="citation_author" content="Tian, Xuetao" /><meta name="citation_author" content="Wang, Rui" /><meta name="citation_keywords" content="DNN testing, test selection, semantic feature, frequency domain, robustness" /><meta name="citation_year" content="2024" /><meta name="citation_volume" content="21" /><meta name="citation_issue" content="4" /><meta name="citation_firstpage" content="1499" /><meta name="citation_lastpage" content="1522" /><meta name="citation_abstract" content="While deep neural networks (DNNs) have great potential for applications in security and safety-critical domains, their limited robustness to adversarial samples and out-of-distribution (OOD) samples raise significant concerns. In the software engineering community, significant efforts have been devoted to devising testing techniques that verify the robustness of DNNs. This paper investigates semantic feature-based test selection for DNNs from a frequency domain perspective and propose a novel method called SaFeTS. Specifically, we leverage saliency detection techniques, such as Fourier Phase Transform to extract semantic features from test cases. These features are then clustered to select diverse test cases to evaluate the robustness of DNNs and model retraining. Experiments on CIFAR-10 and SVHN datasets demonstrate that SaFeTS exposes more varied model errors compared to baseline methods. Further, retraining with SaFeTS-selected samples significantly improves adversarial and out-of-distribution robustness over state-of-the-art test selection methods. 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style="height:20px;width:20px;cursor: pointer; font-weight: bold;" /> <span id="ContentPlaceHolder1_ArticleDetails2_labelArticleCollation"><p align='right'>Computer Science and Information Systems 2024 Volume 21, Issue 4, Pages: 1499-1522<br /> <a href="https://doi.org/10.2298/CSIS230907045J">https://doi.org/10.2298/CSIS230907045J</a><br /><a href='ft.aspx?id=1820-02142400045J' style="text-decoration:underline;">Full text</a> (<img src="img/pdf-tiny.gif" /> 2593 KB)</p></span><br /> <span id="ContentPlaceHolder1_ArticleDetails2_labelArticleTitle"><p><strong>Semantic feature-based test selection for deep neural networks: A frequency domain perspective</strong></p></span> <span id="ContentPlaceHolder1_ArticleDetails2_labelArticleAuthors"><p><i><span style="font-weight: bold;">Jiang Zhouxian</span> (School of Computing and Information Technology, Beijing Jiaotong University, Beijing, China), zhouxianjiang@bjtu.edu.cn<br><span style="font-weight: bold;">Li Honghui</span> (School of Computing and Information Technology, Beijing Jiaotong University, Beijing, China), hhli@bjtu.edu.cn<br><span style="font-weight: bold;">Tian Xuetao</span> (Faculty of Psychology, Beijing Normal University, Beijing, China), xttian@bnu.edu.cn<br><span style="font-weight: bold;">Wang Rui</span> (School of Computing and Information Technology, Beijing Jiaotong University, Beijing, China), rui.wang@bjtu.edu.cn<br></i></p></span> <span id="ContentPlaceHolder1_ArticleDetails2_labelArticleAbstract"><p>While deep neural networks (DNNs) have great potential for applications in security and safety-critical domains, their limited robustness to adversarial samples and out-of-distribution (OOD) samples raise significant concerns. In the software engineering community, significant efforts have been devoted to devising testing techniques that verify the robustness of DNNs. This paper investigates semantic feature-based test selection for DNNs from a frequency domain perspective and propose a novel method called SaFeTS. Specifically, we leverage saliency detection techniques, such as Fourier Phase Transform to extract semantic features from test cases. These features are then clustered to select diverse test cases to evaluate the robustness of DNNs and model retraining. Experiments on CIFAR-10 and SVHN datasets demonstrate that SaFeTS exposes more varied model errors compared to baseline methods. Further, retraining with SaFeTS-selected samples significantly improves adversarial and out-of-distribution robustness over state-of-the-art test selection methods. </p></span> <span id="ContentPlaceHolder1_ArticleDetails2_labelArticleKeywords"><p>Keywords: DNN testing, test selection, semantic feature, frequency domain, robustness</p></span> <span id="ContentPlaceHolder1_ArticleDetails2_labelArticleSCIndeksId"></span> <span id="ContentPlaceHolder1_ArticleDetails2_labelArticleProject"></span> </p> <hr style="border: 1px solid"/> <p> <span id="ContentPlaceHolder1_ArticleDetails2_lblPrikaziReference" class="LblRef" OnClick="ClientClicked()">Show references</span> </p> <div id="RptRef" style="display:none"> Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U.R., et al.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges. 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