DPAttack: Diffused Patch Attacks against Universal Object Detection

• URL: http://arxiv.org/abs/2010.11679v1
• Date: Fri, 16 Oct 2020 04:48:24 GMT
• Title: DPAttack: Diffused Patch Attacks against Universal Object Detection
• Authors: Shudeng Wu, Tao Dai, Shu-Tao Xia
• Abstract summary: Adversarial attacks against object detection can be divided into two categories, whole-pixel attacks and patch attacks. We propose a diffused patch attack (textbfDPAttack) to fool object detectors by diffused patches of asteroid-shaped or grid-shape. Experiments show that our DPAttack can successfully fool most object detectors with diffused patches.
• Score: 66.026630370248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep neural networks (DNNs) have been widely and successfully used in Object Detection, e.g. Faster RCNN, YOLO, CenterNet. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Adversarial attacks against object detection can be divided into two categories, whole-pixel attacks and patch attacks. While these attacks add perturbations to a large number of pixels in images, we proposed a diffused patch attack (\textbf{DPAttack}) to successfully fool object detectors by diffused patches of asteroid-shaped or grid-shape, which only change a small number of pixels. Experiments show that our DPAttack can successfully fool most object detectors with diffused patches and we get the second place in the Alibaba Tianchi competition: Alibaba-Tsinghua Adversarial Challenge on Object Detection. Our code can be obtained from https://github.com/Wu-Shudeng/DPAttack.

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