{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T03:00:52Z","timestamp":1755226852407,"version":"3.43.0"},"reference-count":89,"publisher":"Association for Computing Machinery (ACM)","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"abstract":"<jats:p>\n            Deep learning (DL) components have been broadly applied in diverse applications. Similar to traditional software engineering, effective test case generation methods are needed by industry to enhance the quality and robustness of these deep learning components. To this end, we propose a novel automatic software testing technique,\n            <jats:italic>TAEFuzz<\/jats:italic>\n            (Automatic\n            <jats:underline>Fuzz<\/jats:underline>\n            -Testing via\n            <jats:underline>T<\/jats:underline>\n            ransferable\n            <jats:underline>A<\/jats:underline>\n            dversarial\n            <jats:underline>E<\/jats:underline>\n            xamples), which aims to automatically assess and enhance the robustness of image-based deep learning (DL) systems based on test cases generated by transferable adversarial examples.\n            <jats:italic>TAEFuzz<\/jats:italic>\n            alleviates the over-fitting problem during optimized test case generation and prevents test cases from prematurely falling into local optima. In addition,\n            <jats:italic>TAEFuzz<\/jats:italic>\n            enhances the visual quality of test cases through constraining perturbations inserted into sensitive areas of the images. For a system with low robustness,\n            <jats:italic>TAEFuzz<\/jats:italic>\n            trains a low-cost denoising module to reduce the impact of perturbations in transferable adversarial examples on the system. Experimental results demonstrate that the test cases generated by\n            <jats:italic>TAEFuzz<\/jats:italic>\n            can discover up to 46.1% more errors in the targeted systems, and ensure the visual quality of test cases. Compared to existing techniques,\n            <jats:italic>TAEFuzz<\/jats:italic>\n            also enhances the robustness of the target systems against transferable adversarial examples with the perturbation denoising module.\n          <\/jats:p>","DOI":"10.1145\/3714463","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T13:11:14Z","timestamp":1738069874000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["<i>TAEFuzz<\/i>\n            : Automatic Fuzzing for Image-based Deep Learning Systems via Transferable Adversarial Examples"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-5795","authenticated-orcid":false,"given":"Shunhui","family":"Ji","sequence":"first","affiliation":[{"name":"Hohai University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7651-327X","authenticated-orcid":false,"given":"Changrong","family":"Huang","sequence":"additional","affiliation":[{"name":"Hohai University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2469-5427","authenticated-orcid":false,"given":"Bin","family":"Ren","sequence":"additional","affiliation":[{"name":"Hohai University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7033-5688","authenticated-orcid":false,"given":"Hai","family":"Dong","sequence":"additional","affiliation":[{"name":"RMIT University, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8747-3745","authenticated-orcid":false,"given":"Lars","family":"Grunske","sequence":"additional","affiliation":[{"name":"Humboldt Universit\u00e4t zu Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2563-083X","authenticated-orcid":false,"given":"Yan","family":"Xiao","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3594-408X","authenticated-orcid":false,"given":"Pengcheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hohai University, China"}]}],"member":"320","published-online":{"date-parts":[[2025,1,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 558\u2013567","author":"He Tong","year":"2019","unstructured":"Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li. 2019. Bag of tricks for image classification with convolutional neural networks. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 558\u2013567."},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","article-title":"A survey on deep learning techniques for image and video semantic segmentation","volume":"70","author":"Garcia-Garcia Alberto","year":"2018","unstructured":"Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, Pablo Martinez-Gonzalez, and Jose Garcia-Rodriguez. 2018. A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing 70 (2018), 41\u201365.","journal-title":"Applied Soft Computing"},{"key":"e_1_2_1_3_1","volume-title":"Object detection with deep learning: A review","author":"Zhao Zhong-Qiu","year":"2019","unstructured":"Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu, and Xindong Wu. 2019. Object detection with deep learning: A review. IEEE transactions on neural networks and learning systems 30, 11 (2019), 3212\u20133232."},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 35th IEEE\/ACM international conference on automated software engineering. 1041\u20131052","author":"Berend David","year":"2020","unstructured":"David Berend, Xiaofei Xie, Lei Ma, Lingjun Zhou, Yang Liu, Chi Xu, and Jianjun Zhao. 2020. Cats are not fish: Deep learning testing calls for out-of-distribution awareness. In Proceedings of the 35th IEEE\/ACM international conference on automated software engineering. 1041\u20131052."},{"key":"e_1_2_1_5_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_2_1_6_1","volume-title":"2021 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, 355\u2013367","author":"Riccio Vincenzo","year":"2021","unstructured":"Vincenzo Riccio, Nargiz Humbatova, Gunel Jahangirova, and Paolo Tonella. 2021. Deepmetis: Augmenting a deep learning test set to increase its mutation score. In 2021 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, 355\u2013367."},{"key":"e_1_2_1_7_1","volume-title":"Mitigating adversarial effects through randomization. arXiv preprint arXiv:1711.01991","author":"Xie Cihang","year":"2017","unstructured":"Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. 2017. Mitigating adversarial effects through randomization. arXiv preprint arXiv:1711.01991 (2017)."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. 146\u2013157","author":"Xie Xiaofei","year":"2019","unstructured":"Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Minhui Xue, Hongxu Chen, Yang Liu, Jianjun Zhao, Bo Li, Jianxiong Yin, and Simon See. 2019. Deephunter: a coverage-guided fuzz testing framework for deep neural networks. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. 146\u2013157."},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 702\u2013713","author":"Gerasimou Simos","year":"2020","unstructured":"Simos Gerasimou, Hasan Ferit Eniser, Alper Sen, and Alper Cakan. 2020. Importance-driven deep learning system testing. In Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 702\u2013713."},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 347\u2013358","author":"Zhou Husheng","year":"2020","unstructured":"Husheng Zhou, Wei Li, Zelun Kong, Junfeng Guo, Yuqun Zhang, Bei Yu, Lingming Zhang, and Cong Liu. 2020. Deepbillboard: Systematic physical-world testing of autonomous driving systems. In Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 347\u2013358."},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. 177\u2013188","author":"Feng Yang","year":"2020","unstructured":"Yang Feng, Qingkai Shi, Xinyu Gao, Jun Wan, Chunrong Fang, and Zhenyu Chen. 2020. Deepgini: prioritizing massive tests to enhance the robustness of deep neural networks. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. 177\u2013188."},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis. 42\u201355","author":"Zhao Zhe","year":"2021","unstructured":"Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, and Jun Sun. 2021. Attack as defense: Characterizing adversarial examples using robustness. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis. 42\u201355."},{"key":"e_1_2_1_13_1","volume-title":"2012 34th International Conference on Software Engineering (ICSE). IEEE, 3\u201313","author":"Goues Claire Le","year":"2012","unstructured":"Claire Le Goues, Michael Dewey-Vogt, Stephanie Forrest, and Westley Weimer. 2012. A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each. In 2012 34th International Conference on Software Engineering (ICSE). IEEE, 3\u201313."},{"key":"e_1_2_1_14_1","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/TSE.2020.2998785","article-title":"Quality of Automated Program Repair on Real-World Defects","volume":"48","author":"Motwani Manish","year":"2022","unstructured":"Manish Motwani, Mauricio Soto, Yuriy Brun, Rene Just, and Claire Le Goues. 2022. Quality of Automated Program Repair on Real-World Defects. IEEE Transactions on Software Engineering 48, 02 (2022), 637\u2013661.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_2_1_15_1","volume-title":"2009 IEEE 31st International Conference on Software Engineering. IEEE, 364\u2013374","author":"Weimer Westley","year":"2009","unstructured":"Westley Weimer, ThanhVu Nguyen, Claire Le Goues, and Stephanie Forrest. 2009. Automatically finding patches using genetic programming. In 2009 IEEE 31st International Conference on Software Engineering. IEEE, 364\u2013374."},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/TSE.2016.2560811","article-title":"Nopol: Automatic repair of conditional statement bugs in java programs","volume":"43","author":"Xuan Jifeng","year":"2016","unstructured":"Jifeng Xuan, Matias Martinez, Favio Demarco, Maxime Clement, Sebastian Lamelas Marcote, Thomas Durieux, Daniel Le Berre, and Martin Monperrus. 2016. Nopol: Automatic repair of conditional statement bugs in java programs. IEEE Transactions on Software Engineering 43, 1 (2016), 34\u201355.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. 166\u2013178","author":"Long Fan","year":"2015","unstructured":"Fan Long and Martin Rinard. 2015. Staged program repair with condition synthesis. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. 166\u2013178."},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the 40th International Conference on Software Engineering. 129\u2013139","author":"Mechtaev Sergey","year":"2018","unstructured":"Sergey Mechtaev, Manh-Dung Nguyen, Yannic Noller, Lars Grunske, and Abhik Roychoudhury. 2018. Semantic program repair using a reference implementation. In Proceedings of the 40th International Conference on Software Engineering. 129\u2013139."},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the 38th international conference on software engineering. 691\u2013701","author":"Mechtaev Sergey","year":"2016","unstructured":"Sergey Mechtaev, Jooyong Yi, and Abhik Roychoudhury. 2016. Angelix: Scalable multiline program patch synthesis via symbolic analysis. In Proceedings of the 38th international conference on software engineering. 691\u2013701."},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. 390\u2013405","author":"Shariffdeen Ridwan","year":"2021","unstructured":"Ridwan Shariffdeen, Yannic Noller, Lars Grunske, and Abhik Roychoudhury. 2021. Concolic program repair. In Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. 390\u2013405."},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 739\u2013743","author":"Guo Jianmin","year":"2018","unstructured":"Jianmin Guo, Yu Jiang, Yue Zhao, Quan Chen, and Jiaguang Sun. 2018. Dlfuzz: Differential fuzzing testing of deep learning systems. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 739\u2013743."},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the 26th ACM SIGSOFT international symposium on software testing and analysis. 226\u2013236","author":"Xin Qi","year":"2017","unstructured":"Qi Xin and Steven P Reiss. 2017. Identifying test-suite-overfitted patches through test case generation. In Proceedings of the 26th ACM SIGSOFT international symposium on software testing and analysis. 226\u2013236."},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the 40th international conference on software engineering. 789\u2013799","author":"Xiong Yingfei","year":"2018","unstructured":"Yingfei Xiong, Xinyuan Liu, Muhan Zeng, Lu Zhang, and Gang Huang. 2018. Identifying patch correctness in test-based program repair. In Proceedings of the 40th international conference on software engineering. 789\u2013799."},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10664-018-9619-4","article-title":"Alleviating patch overfitting with automatic test generation: a study of feasibility and effectiveness for the Nopol repair system","volume":"24","author":"Yu Zhongxing","year":"2019","unstructured":"Zhongxing Yu, Matias Martinez, Benjamin Danglot, Thomas Durieux, and Martin Monperrus. 2019. Alleviating patch overfitting with automatic test generation: a study of feasibility and effectiveness for the Nopol repair system. Empirical Software Engineering 24 (2019), 33\u201367.","journal-title":"Empirical Software Engineering"},{"key":"e_1_2_1_25_1","volume-title":"Fuzzing: the Past, the Present and the Future. Actes du","author":"Takanen Ari","year":"2009","unstructured":"Ari Takanen. 2009. Fuzzing: the Past, the Present and the Future. Actes du (2009), 202\u2013212."},{"key":"e_1_2_1_26_1","volume-title":"Proceedings of the acm\/ieee 42nd international conference on software engineering. 1147\u20131158","author":"Gao Xiang","year":"2020","unstructured":"Xiang Gao, Ripon K Saha, Mukul R Prasad, and Abhik Roychoudhury. 2020. Fuzz testing based data augmentation to improve robustness of deep neural networks. In Proceedings of the acm\/ieee 42nd international conference on software engineering. 1147\u20131158."},{"key":"e_1_2_1_27_1","volume-title":"2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, 300\u2013311","author":"Wang Jingyi","year":"2021","unstructured":"Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, and Peng Cheng. 2021. RobOT: Robustness-oriented testing for deep learning systems. In 2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, 300\u2013311."},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. 165\u2013176","author":"Lee Seokhyun","year":"2020","unstructured":"Seokhyun Lee, Sooyoung Cha, Dain Lee, and Hakjoo Oh. 2020. Effective white-box testing of deep neural networks with adaptive neuron-selection strategy. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. 165\u2013176."},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 33rd ACM\/IEEE international conference on automated software engineering. 120\u2013131","author":"Ma Lei","year":"2018","unstructured":"Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, et al. 2018. Deepgauge: Multi-granularity testing criteria for deep learning systems. In Proceedings of the 33rd ACM\/IEEE international conference on automated software engineering. 120\u2013131."},{"key":"e_1_2_1_30_1","volume-title":"proceedings of the 26th Symposium on Operating Systems Principles. 1\u201318","author":"Pei Kexin","year":"2017","unstructured":"Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. 2017. Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles. 1\u201318."},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39\u201357.","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 876\u2013888","author":"Riccio Vincenzo","year":"2020","unstructured":"Vincenzo Riccio and Paolo Tonella. 2020. Model-based exploration of the frontier of behaviours for deep learning system testing. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 876\u2013888."},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 949\u2013960","author":"Zhang Peixin","year":"2020","unstructured":"Peixin Zhang, Jingyi Wang, Jun Sun, Guoliang Dong, Xinyu Wang, Xingen Wang, Jin Song Dong, and Ting Dai. 2020. White-box fairness testing through adversarial sampling. In Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 949\u2013960."},{"key":"e_1_2_1_34_1","volume-title":"Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 739\u2013751","author":"Zhang Xiyue","year":"2020","unstructured":"Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, and Meng Sun. 2020. Towards characterizing adversarial defects of deep learning software from the lens of uncertainty. In Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering. 739\u2013751."},{"key":"e_1_2_1_35_1","volume-title":"International Conference on Machine Learning. PMLR, 2484\u20132493","author":"Guo Chuan","year":"2019","unstructured":"Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, and Kilian Weinberger. 2019. Simple black-box adversarial attacks. In International Conference on Machine Learning. PMLR, 2484\u20132493."},{"key":"e_1_2_1_36_1","first-page":"12288","article-title":"Learning black-box attackers with transferable priors and query feedback","volume":"33","author":"Yang Jiancheng","year":"2020","unstructured":"Jiancheng Yang, Yangzhou Jiang, Xiaoyang Huang, Bingbing Ni, and Chenglong Zhao. 2020. Learning black-box attackers with transferable priors and query feedback. Advances in Neural Information Processing Systems 33 (2020), 12288\u201312299.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_37_1","volume-title":"Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 800\u2013812","author":"Zhang Fuyuan","year":"2020","unstructured":"Fuyuan Zhang, Sankalan Pal Chowdhury, and Maria Christakis. 2020. Deepsearch: A simple and effective blackbox attack for deep neural networks. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 800\u2013812."},{"key":"e_1_2_1_38_1","volume-title":"International Conference on Machine Learning. PMLR, 5498\u20135507","author":"Roth Kevin","year":"2019","unstructured":"Kevin Roth, Yannic Kilcher, and Thomas Hofmann. 2019. The odds are odd: A statistical test for detecting adversarial examples. In International Conference on Machine Learning. PMLR, 5498\u20135507."},{"key":"e_1_2_1_39_1","volume-title":"Feature Space Perturbations Yield More Transferable Adversarial Examples. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7059\u20137067","author":"Inkawhich Nathan","year":"2019","unstructured":"Nathan Inkawhich, Wei Wen, Hai Helen Li, and Yiran Chen. 2019. Feature Space Perturbations Yield More Transferable Adversarial Examples. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 7059\u20137067. doi:10.1109\/CVPR.2019.00723"},{"key":"e_1_2_1_40_1","unstructured":"Lei Wu Zhanxing Zhu Cheng Tai and Weinan E. 2018. Understanding and Enhancing the Transferability of Adversarial Examples. arXiv:1802.09707 [stat.ML] https:\/\/arxiv.org\/abs\/1802.09707"},{"key":"e_1_2_1_41_1","volume-title":"Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770","author":"Liu Yanpei","year":"2016","unstructured":"Yanpei Liu, Xinyun Chen, Chang Liu, and Dawn Song. 2016. Delving into transferable adversarial examples and black-box attacks. arXiv preprint arXiv:1611.02770 (2016)."},{"key":"e_1_2_1_42_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 9185\u20139193","author":"Dong Yinpeng","year":"2018","unstructured":"Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li. 2018. Boosting adversarial attacks with momentum. In Proceedings of the IEEE conference on computer vision and pattern recognition. 9185\u20139193."},{"key":"e_1_2_1_43_1","volume-title":"Nesterov accelerated gradient and scale invariance for adversarial attacks. arXiv preprint arXiv:1908.06281","author":"Lin Jiadong","year":"2019","unstructured":"Jiadong Lin, Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft. 2019. Nesterov accelerated gradient and scale invariance for adversarial attacks. arXiv preprint arXiv:1908.06281 (2019)."},{"key":"e_1_2_1_44_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2730\u20132739","author":"Xie Cihang","year":"2019","unstructured":"Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille. 2019. Improving transferability of adversarial examples with input diversity. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2730\u20132739."},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4312\u20134321","author":"Dong Yinpeng","year":"2019","unstructured":"Yinpeng Dong, Tianyu Pang, Hang Su, and Jun Zhu. 2019. Evading defenses to transferable adversarial examples by translation-invariant attacks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4312\u20134321."},{"key":"e_1_2_1_46_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1924\u20131933","author":"Wang Xiaosen","year":"2021","unstructured":"Xiaosen Wang and Kun He. 2021. Enhancing the transferability of adversarial attacks through variance tuning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1924\u20131933."},{"key":"e_1_2_1_47_1","volume-title":"Towards deep learning models resistant to adversarial attacks. stat 1050, 9","author":"M\u0105adry Aleksander","year":"2017","unstructured":"Aleksander M\u0105adry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. stat 1050, 9 (2017)."},{"key":"e_1_2_1_48_1","volume-title":"International conference on machine learning. PMLR, 2206\u20132216","author":"Croce Francesco","year":"2020","unstructured":"Francesco Croce and Matthias Hein. 2020. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning. PMLR, 2206\u20132216."},{"key":"e_1_2_1_49_1","volume-title":"Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 851\u2013862","author":"Harel-Canada Fabrice","year":"2020","unstructured":"Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, and Miryung Kim. 2020. Is neuron coverage a meaningful measure for testing deep neural networks?. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 851\u2013862."},{"key":"e_1_2_1_50_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision. 4741\u20134750","author":"Zhu Hegui","year":"2023","unstructured":"Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, and Wuming Jiang. 2023. Boosting adversarial transferability via gradient relevance attack. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 4741\u20134750."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3319389"},{"key":"e_1_2_1_52_1","volume-title":"Advances in Neural Information Processing Systems","author":"Jia Shuai","year":"2022","unstructured":"Shuai Jia, Bangjie Yin, Taiping Yao, Shouhong Ding, Chunhua Shen, Xiaokang Yang, and Chao Ma. 2022. Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 34136\u201334147. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/dccbeb7a8df3065c4646928985edf435-Paper-Conference.pdf"},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision. 1833\u20131844","author":"Liang Jingyun","year":"2021","unstructured":"Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. 2021. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE\/CVF international conference on computer vision. 1833\u20131844."},{"key":"e_1_2_1_54_1","doi-asserted-by":"crossref","first-page":"4630","DOI":"10.1109\/TSE.2021.3124006","article-title":"Cagfuzz: coverage-guided adversarial generative fuzzing testing for image-based deep learning systems","volume":"48","author":"Zhang Pengcheng","year":"2021","unstructured":"Pengcheng Zhang, Bin Ren, Hai Dong, and Qiyin Dai. 2021. Cagfuzz: coverage-guided adversarial generative fuzzing testing for image-based deep learning systems. IEEE Transactions on Software Engineering 48, 11 (2021), 4630\u20134646.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_2_1_55_1","volume-title":"Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533","author":"Kurakin Alexey","year":"2016","unstructured":"Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016. Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 (2016)."},{"key":"e_1_2_1_56_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 1778\u20131787","author":"Liao Fangzhou","year":"2018","unstructured":"Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu. 2018. Defense against adversarial attacks using high-level representation guided denoiser. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1778\u20131787."},{"key":"e_1_2_1_57_1","volume-title":"Towards deep learning models resistant to adversarial attacks. stat 1050, 9","author":"M\u0105adry Aleksander","year":"2017","unstructured":"Aleksander M\u0105adry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. stat 1050, 9 (2017)."},{"key":"e_1_2_1_58_1","volume-title":"Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994","author":"Wong Eric","year":"2020","unstructured":"Eric Wong, Leslie Rice, and J Zico Kolter. 2020. Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994 (2020)."},{"key":"e_1_2_1_59_1","volume-title":"international conference on machine learning. PMLR, 1310\u20131320","author":"Cohen Jeremy","year":"2019","unstructured":"Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. 2019. Certified adversarial robustness via randomized smoothing. In international conference on machine learning. PMLR, 1310\u20131320."},{"key":"e_1_2_1_60_1","volume-title":"International Conference on Machine Learning. PMLR, 10693\u201310705","author":"Yang Greg","year":"2020","unstructured":"Greg Yang, Tony Duan, J Edward Hu, Hadi Salman, Ilya Razenshteyn, and Jerry Li. 2020. Randomized smoothing of all shapes and sizes. In International Conference on Machine Learning. PMLR, 10693\u201310705."},{"key":"e_1_2_1_61_1","volume-title":"A study of the effect of jpg compression on adversarial images. arXiv preprint arXiv:1608.00853","author":"Dziugaite Gintare Karolina","year":"2016","unstructured":"Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel M Roy. 2016. A study of the effect of jpg compression on adversarial images. arXiv preprint arXiv:1608.00853 (2016)."},{"key":"e_1_2_1_62_1","volume-title":"Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo, Mayank Rana, Moustapha Cisse, and Laurens Van Der Maaten. 2017. Countering adversarial images using input transformations. arXiv preprint arXiv:1711.00117 (2017)."},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 262\u2013271","author":"Naseer Muzammal","year":"2020","unstructured":"Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli. 2020. A self-supervised approach for adversarial robustness. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 262\u2013271."},{"key":"e_1_2_1_64_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"32","author":"Luo Bo","year":"2018","unstructured":"Bo Luo, Yannan Liu, Lingxiao Wei, and Qiang Xu. 2018. Towards imperceptible and robust adversarial example attacks against neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32."},{"key":"e_1_2_1_65_1","volume-title":"arXiv preprint arXiv:2012.15503","author":"Gao Lianli","year":"2020","unstructured":"Lianli Gao, Qilong Zhang, Jingkuan Song, and Heng Tao Shen. 2020. Patch-wise++ perturbation for adversarial targeted attacks. arXiv preprint arXiv:2012.15503 (2020)."},{"key":"e_1_2_1_66_1","volume-title":"mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412","author":"Zhang Hongyi","year":"2017","unstructured":"Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)."},{"key":"e_1_2_1_67_1","doi-asserted-by":"crossref","first-page":"6360","DOI":"10.1109\/TPAMI.2021.3088914","article-title":"Plug-and-play image restoration with deep denoiser prior","volume":"44","author":"Zhang Kai","year":"2021","unstructured":"Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte. 2021. Plug-and-play image restoration with deep denoiser prior. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 10 (2021), 6360\u20136376.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_1_68_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 773\u2013782","author":"Liu Pengju","year":"2018","unstructured":"Pengju Liu, Hongzhi Zhang, Kai Zhang, Liang Lin, and Wangmeng Zuo. 2018. Multi-level wavelet-CNN for image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 773\u2013782."},{"key":"e_1_2_1_69_1","volume-title":"Residual non-local attention networks for image restoration. arXiv preprint arXiv:1903.10082","author":"Zhang Yulun","year":"2019","unstructured":"Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, and Yun Fu. 2019. Residual non-local attention networks for image restoration. arXiv preprint arXiv:1903.10082 (2019)."},{"key":"e_1_2_1_70_1","volume-title":"The mnist database of handwritten digit images for machine learning research [best of the web]","author":"Deng Li","year":"2012","unstructured":"Li Deng. 2012. The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE signal processing magazine 29, 6 (2012), 141\u2013142."},{"key":"e_1_2_1_71_1","volume-title":"Cifar10-dvs: an event-stream dataset for object classification. Frontiers in neuroscience 11","author":"Li Hongmin","year":"2017","unstructured":"Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, and Luping Shi. 2017. Cifar10-dvs: an event-stream dataset for object classification. Frontiers in neuroscience 11 (2017), 309."},{"key":"e_1_2_1_72_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 2818\u20132826","author":"Szegedy Christian","year":"2016","unstructured":"Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818\u20132826."},{"key":"e_1_2_1_73_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence","volume":"31","author":"Szegedy Christian","year":"2017","unstructured":"Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, Vol. 31."},{"key":"e_1_2_1_74_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 770\u2013778","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770\u2013778."},{"key":"e_1_2_1_75_1","volume-title":"Ensemble Adversarial Training: Attacks and Defenses. In International Conference on Learning Representations.","author":"Tram\u00e8r Florian","year":"2018","unstructured":"Florian Tram\u00e8r, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2018. Ensemble Adversarial Training: Attacks and Defenses. In International Conference on Learning Representations."},{"key":"e_1_2_1_76_1","volume-title":"Proceedings, Part XXVIII 16","author":"Gao Lianli","year":"2020","unstructured":"Lianli Gao, Qilong Zhang, Jingkuan Song, Xianglong Liu, and Heng Tao Shen. 2020. Patch-wise attack for fooling deep neural network. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXVIII 16. Springer, 307\u2013322."},{"key":"e_1_2_1_77_1","volume-title":"Proceedings of the 40th international conference on software engineering. 303\u2013314","author":"Tian Yuchi","year":"2018","unstructured":"Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray. 2018. Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In Proceedings of the 40th international conference on software engineering. 303\u2013314."},{"key":"e_1_2_1_78_1","volume-title":"International Conference on Machine Learning. PMLR, 4901\u20134911","author":"Odena Augustus","year":"2019","unstructured":"Augustus Odena, Catherine Olsson, David Andersen, and Ian Goodfellow. 2019. Tensorfuzz: Debugging neural networks with coverage-guided fuzzing. In International Conference on Machine Learning. PMLR, 4901\u20134911."},{"key":"e_1_2_1_79_1","volume-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 860\u2013868","author":"Liu Zihao","year":"2019","unstructured":"Zihao Liu, Qi Liu, Tao Liu, Nuo Xu, Xue Lin, Yanzhi Wang, and Wujie Wen. 2019. Feature distillation: Dnn-oriented jpeg compression against adversarial examples. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 860\u2013868."},{"key":"e_1_2_1_80_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 6084\u20136092","author":"Jia Xiaojun","year":"2019","unstructured":"Xiaojun Jia, Xingxing Wei, Xiaochun Cao, and Hassan Foroosh. 2019. Comdefend: An efficient image compression model to defend adversarial examples. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 6084\u20136092."},{"key":"e_1_2_1_81_1","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision. 4724\u20134732","author":"Croce Francesco","year":"2019","unstructured":"Francesco Croce and Matthias Hein. 2019. Sparse and imperceivable adversarial attacks. In Proceedings of the IEEE\/CVF international conference on computer vision. 4724\u20134732."},{"key":"e_1_2_1_82_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 586\u2013595","author":"Zhang Richard","year":"2018","unstructured":"Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition. 586\u2013595."},{"key":"e_1_2_1_83_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1039\u20131048","author":"Zhao Zhengyu","year":"2020","unstructured":"Zhengyu Zhao, Zhuoran Liu, and Martha Larson. 2020. Towards large yet imperceptible adversarial image perturbations with perceptual color distance. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1039\u20131048."},{"key":"e_1_2_1_84_1","volume-title":"The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research & Application: Endorsed by Inter-Society Color Council","author":"Luo M Ronnier","year":"2001","unstructured":"M Ronnier Luo, Guihua Cui, and Bryan Rigg. 2001. The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research & Application: Endorsed by Inter-Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Fran\u00e7ais de la Couleur 26, 5 (2001), 340\u2013350."},{"key":"e_1_2_1_85_1","doi-asserted-by":"crossref","unstructured":"Z. Wang A. C. Bovik H. R. Sheikh and E. P. Simoncelli. 2004. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing 13 (2004).","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_2_1_86_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 641\u2013649","author":"Li Maosen","year":"2020","unstructured":"Maosen Li, Cheng Deng, Tengjiao Li, Junchi Yan, Xinbo Gao, and Heng Huang. 2020. Towards transferable targeted attack. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 641\u2013649."},{"key":"e_1_2_1_87_1","volume-title":"Exploring memorization in adversarial training. arXiv preprint arXiv:2106.01606","author":"Dong Yinpeng","year":"2021","unstructured":"Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, and Jun Zhu. 2021. Exploring memorization in adversarial training. arXiv preprint arXiv:2106.01606 (2021)."},{"key":"e_1_2_1_88_1","volume-title":"International conference on machine learning. PMLR, 7472\u20137482","author":"Zhang Hongyang","year":"2019","unstructured":"Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019. Theoretically principled trade-off between robustness and accuracy. In International conference on machine learning. PMLR, 7472\u20137482."},{"key":"e_1_2_1_89_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 1625\u20131634","author":"Eykholt Kevin","year":"2018","unstructured":"Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2018. Robust physical-world attacks on deep learning visual classification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1625\u20131634."}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3714463","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T13:12:01Z","timestamp":1738069921000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3714463"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,28]]},"references-count":89,"alternative-id":["10.1145\/3714463"],"URL":"https:\/\/doi.org\/10.1145\/3714463","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"type":"print","value":"1049-331X"},{"type":"electronic","value":"1557-7392"}],"subject":[],"published":{"date-parts":[[2025,1,28]]},"assertion":[{"value":"2024-03-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3714463"}}