{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T22:22:59Z","timestamp":1769120579623,"version":"3.49.0"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research Development Project","award":["2018AAA0100702, 2019YFB1311503"],"award-info":[{"award-number":["2018AAA0100702, 2019YFB1311503"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61977046, 61876107, U1803261"],"award-info":[{"award-number":["61977046, 61876107, U1803261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/tpami.2020.3033291","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T19:49:20Z","timestamp":1603482560000},"page":"1-1","source":"Crossref","is-referenced-by-count":62,"title":["Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet"],"prefix":"10.1109","author":[{"given":"Sizhe","family":"Chen","sequence":"first","affiliation":[]},{"given":"Zhengbao","family":"He","sequence":"additional","affiliation":[]},{"given":"Chengjin","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiaolin","family":"Huang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2807385"},{"key":"ref2","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goodfellow"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2936378"},{"key":"ref6","article-title":"Improving black-box adversarial attacks with a transfer-based prior","author":"Cheng","year":"2019"},{"key":"ref7","article-title":"Prior convictions: Black-box adversarial attacks with bandits and priors","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Ilyas"},{"key":"ref8","first-page":"3820","article-title":"Subspace attack: Exploiting promising subspaces for query-efficient black-box attacks","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Guo"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.17"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00444"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00284"},{"key":"ref14","article-title":"Nesterov accelerated gradient and scale invariance for adversarial attacks","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Lin"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_39"},{"key":"ref16","first-page":"7502","article-title":"Interpreting adversarially trained convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref18","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Simonyan"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.241"},{"key":"ref21","article-title":"Certifiable distributional robustness with principled adversarial training","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Sinha"},{"key":"ref22","article-title":"Intriguing properties of neural networks","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Szegedy"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"ref25","first-page":"8322","article-title":"Constructing unrestricted adversarial examples with generative models","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Song"},{"key":"ref26","article-title":"Adversarial transformation networks: Learning to generate adversarial examples","author":"Baluja","year":"2017"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00526"},{"key":"ref28","article-title":"Adversarial training methods for semi-supervised text classification","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Miyato"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11688"},{"key":"ref30","first-page":"227","article-title":"You only propagate once: Painless adversarial training using maximal principle","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00191"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00059"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00095"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00894"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2940533"},{"key":"ref36","article-title":"Transferability in machine learning: From phenomena to black-box attacks using adversarial samples","author":"Papernot","year":"early access, 2016"},{"key":"ref37","article-title":"Decision-based adversarial attacks: Reliable attacks against black-box machine learning models","author":"Brendel","year":"2018"},{"key":"ref38","first-page":"2137","article-title":"Black-box adversarial attacks with limited queries and information","volume":"80","author":"Ilyas"},{"key":"ref39","article-title":"Bayesopt adversarial attack","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ru"},{"key":"ref40","article-title":"Yet another but more efficient black-box adversarial attack: Tiling and evolution strategies","author":"Meunier","year":"2019"},{"key":"ref41","article-title":"Query-efficient meta attack to deep neural networks","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Du"},{"key":"ref42","article-title":"Skip connections matter: On the transferability of adversarial examples generated with resnets","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wu"},{"key":"ref43","first-page":"6000","article-title":"Attention is all you need","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Vaswani"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref46","article-title":"Network in network","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Lin"},{"key":"ref47","article-title":"Object detectors emerge in deep scene CNNs","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Zhou"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3547"},{"key":"ref50","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Simonyan"},{"key":"ref51","article-title":"Striving for simplicity: The all convolutional net","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Springenberg"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20893-6_8"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00513"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01501"},{"key":"ref58","first-page":"9453","article-title":"Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Barbu"},{"key":"ref59","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hendrycks"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref64","article-title":"Keras","author":"Chollet","year":"2015"},{"key":"ref65","article-title":"TensorFlow: Large-scale mchine learning on heterogeneous systems","author":"Abadi","year":"2015"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"},{"key":"ref67","article-title":"Countering adversarial images using input transformations","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Guo"},{"key":"ref68","first-page":"1310","article-title":"Certified adversarial robustness via randomized smoothing","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Cohen"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1201\/9781351251389-8"},{"key":"ref70","article-title":"Mitigating adversarial effects through randomization","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Xie"},{"key":"ref71","article-title":"Ensemble adversarial training: Attacks and defenses","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Tram\u00e8r"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/09238430.pdf?arnumber=9238430","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:13:44Z","timestamp":1704842024000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9238430\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":71,"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3033291","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}