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Shape Matters: Deformable Patch Attack

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Book cover Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13664))

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Abstract

Though deep neural networks (DNNs) have demonstrated excellent performance in computer vision, they are susceptible and vulnerable to carefully crafted adversarial examples which can mislead DNNs to incorrect outputs. Patch attack is one of the most threatening forms, which has the potential to threaten the security of real-world systems. Previous work always assumes patches to have fixed shapes, such as circles or rectangles, and it does not consider the shape of patches as a factor in patch attacks. To explore this issue, we propose a novel Deformable Patch Representation (DPR) that can harness the geometric structure of triangles to support the differentiable mapping between contour modeling and masks. Moreover, we introduce a joint optimization algorithm, named Deformable Adversarial Patch (DAPatch), which allows simultaneous and efficient optimization of shape and texture to enhance attack performance. We show that even with a small area, a particular shape can improve attack performance. Therefore, DAPatch achieves state-of-the-art attack performance by deforming shapes on GTSRB and ILSVRC2012 across various network architectures, and the generated patches can be threatening in the real world.

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Acknowledgements

This work was done when Zhaoyu Chen was an intern at Youtu Lab, Tencent. This work was supported by National Natural Science Foundation of China (No. 62072112), Scientific and Technological Innovation Action Plan of Shanghai Science and Technology Committee (No. 20511103102), Fudan University-CIOMP Joint Fund (No. FC2019-005), and Double First-class Construction Fund (No. XM03211178).

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Authors and Affiliations

  1. Academy for Engineering and Technology Fudan University, Shanghai, China

    Zhaoyu Chen & Wenqiang Zhang

  2. Youtu Lab, Tencent, Shenzhen, China

    Bo Li, Shuang Wu, Jianghe Xu & Shouhong Ding

  3. Yiwu Research Institute of Fudan University, Yiwu, China

    Wenqiang Zhang

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  1. Zhaoyu Chen
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  2. Bo Li
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  3. Shuang Wu
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  4. Jianghe Xu
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  5. Shouhong Ding
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  6. Wenqiang Zhang
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Correspondence to Bo Li .

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  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Chen, Z., Li, B., Wu, S., Xu, J., Ding, S., Zhang, W. (2022). Shape Matters: Deformable Patch Attack. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_31

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