Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection

• URL: http://arxiv.org/abs/2201.08970v1
• Date: Sat, 22 Jan 2022 06:00:17 GMT
• Title: Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection
• Authors: Siyuan Liang, Baoyuan Wu, Yanbo Fan, Xingxing Wei, Xiaochun Cao
• Abstract summary: We propose a Parallel Rectangle Flip Attack (PRFA) via random search to avoid sub-optimal detection near the attacked region. Our method can effectively and efficiently attack various popular object detectors, including anchor-based and anchor-free, and generate transferable adversarial examples.
• Score: 89.08832589750003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection has been widely used in many safety-critical tasks, such as autonomous driving. However, its vulnerability to adversarial examples has not been sufficiently studied, especially under the practical scenario of black-box attacks, where the attacker can only access the query feedback of predicted bounding-boxes and top-1 scores returned by the attacked model. Compared with black-box attack to image classification, there are two main challenges in black-box attack to detection. Firstly, even if one bounding-box is successfully attacked, another sub-optimal bounding-box may be detected near the attacked bounding-box. Secondly, there are multiple bounding-boxes, leading to very high attack cost. To address these challenges, we propose a Parallel Rectangle Flip Attack (PRFA) via random search. We explain the difference between our method with other attacks in Fig.~\ref{fig1}. Specifically, we generate perturbations in each rectangle patch to avoid sub-optimal detection near the attacked region. Besides, utilizing the observation that adversarial perturbations mainly locate around objects' contours and critical points under white-box attacks, the search space of attacked rectangles is reduced to improve the attack efficiency. Moreover, we develop a parallel mechanism of attacking multiple rectangles simultaneously to further accelerate the attack process. Extensive experiments demonstrate that our method can effectively and efficiently attack various popular object detectors, including anchor-based and anchor-free, and generate transferable adversarial examples.

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