{"id":1191130,"date":"2023-01-26T00:00:00","date_gmt":"2023-01-26T08:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/attacking-important-pixels-for-anchorae%c2%90free-detectors\/"},"modified":"2026-10-27T16:10:31","modified_gmt":"2026-10-27T23:10:31","slug":"attacking-important-pixels-for-anchorae%c2%90free-detectors","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/attacking-important-pixels-for-anchorae%c2%90free-detectors\/","title":{"rendered":"Attacking Important Pixels for Anchor-free Detectors"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change the prediction result. Existing adversarial attacks on object detection focus on attacking anchor-based detectors, which may not work well for anchor-free detectors. In this paper, we propose the first adversarial attack dedicated to anchor-free detectors. It is a category-wise attack that attacks important pixels of all instances of a category simultaneously. Our attack manifests in two forms, sparse category-wise attack (SCA) and dense category-wise attack (DCA), that minimize the <math><mrow><msub><mi>L<\/mi><mn>0<\/mn><\/msub><\/mrow><\/math> and <math><mrow><msub><mi>L<\/mi><mi>\u221e<\/mi><\/msub><\/mrow><\/math> norm-based perturbations, respectively. For DCA, we present three variants, DCA-G, DCA-L, and DCA-S, that select a global region, a local region, and a semantic region, respectively, to attack. Our experiments on large-scale benchmark datasets including PascalVOC, MS-COCO, and MS-COCO Keypoints indicate that our proposed methods achieve state-of-the-art attack performance and transferability on both object detection and human pose estimation tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change the prediction result. Existing adversarial attacks on object detection focus on attacking anchor-based detectors, which may not work well for anchor-free detectors. In this paper, we propose the first adversarial attack dedicated to anchor-free detectors. It is a [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Yufei Xie","user_id":0},{"type":"text","value":"Shu Hu","user_id":0},{"type":"user_nicename","value":"Xin Wang","user_id":"41146"},{"type":"text","value":"Quanyu Liao","user_id":0},{"type":"user_nicename","value":"Bin Zhu","user_id":"31240"},{"type":"text","value":"Xi Wu","user_id":0},{"type":"text","value":"Siwei 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