Attacking Important Pixels for Anchor-free Detectors

• URL: http://arxiv.org/abs/2301.11457v1
• Date: Thu, 26 Jan 2023 23:03:03 GMT
• Title: Attacking Important Pixels for Anchor-free Detectors
• Authors: Yunxu Xie, Shu Hu, Xin Wang, Quanyu Liao, Bin Zhu, Xi Wu, Siwei Lyu
• Abstract summary: Existing adversarial attacks on object detection focus on attacking anchor-based detectors. We propose the first adversarial attack dedicated to anchor-free detectors. Our proposed methods achieve state-of-the-art attack performance and transferability on both object detection and human pose estimation tasks.
• Score: 47.524554948433995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $L_0$ and $L_\infty$ 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.

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