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Transferable Black-Box Attack Against Face Recognition With Spatial Mutable Adversarial Patch
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DOI：10.1109/TIFS.2023.3310352.png)
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Deep Neural Networks (DNNs) are vulnerable to adversarial patch attacks, which raises security concerns for face recognition systems using DNNs. Previous attack methods focus on the perturbation texture and generate adversarial patches with fixed shapes at random or pre-designed locations, which causes poor adversarial transferability. This paper proposes a Spatial Mutable Adversarial Patch (SMAP) method to generate a dynamic mutable patch to be injected into the face. In the proposed SMAP, the texture, position and shape of the patch are optimized simultaneously and the patch generation pipeline is end-to-end differentiable. Specifically, a Patch Location Selection Scheme is designed to find the critical patch position with the most significant influence on the target identity by the step-based gradient search. By innovatively bridging the pre-defined mask and the dynamic update of the patch, the patch position and shape are changed based on the affine transformation and sampling mechanism in each iteration, which maintains the importance of the injected patch to the adversarial objective. To evaluate the vulnerability of face recognition models, we explore more threatening impersonation attacks under the black-box setting and design a strict evaluation metric that aligns with the real-world scenario. Extensive experiments show that the proposed SMAP improves attack performance across various face recognition models and datasets. Moreover, SMAP achieves better transferability on commercial face recognition systems than existing methods.
Keyword:
Adversarial patch
face recognition
impersonation attack
joint optimization
spatial mutability
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5.3K
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