SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Models

• URL: http://arxiv.org/abs/2502.19710v1
• Date: Thu, 27 Feb 2025 02:57:29 GMT
• Title: SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Models
• Authors: Mingsi Wang, Shuaiyin Yao, Chang Yue, Lijie Zhang, Guozhu Meng,
• Abstract summary: Impersonation attacks are a significant threat because adversarial perturbations allow attackers to disguise themselves as legitimate users.<n>We propose a novel method to generate adversarial patches via semantic perturbations in the latent space rather than direct pixel manipulation.<n>Our method achieves an average attack success rate improvement of 45.66%, and a reduction in the number of queries by about 40%.
• Score: 4.970240615354004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given the need to evaluate the robustness of face recognition (FR) models, many efforts have focused on adversarial patch attacks that mislead FR models by introducing localized perturbations. Impersonation attacks are a significant threat because adversarial perturbations allow attackers to disguise themselves as legitimate users. This can lead to severe consequences, including data breaches, system damage, and misuse of resources. However, research on such attacks in FR remains limited. Existing adversarial patch generation methods exhibit limited efficacy in impersonation attacks due to (1) the need for high attacker capabilities, (2) low attack success rates, and (3) excessive query requirements. To address these challenges, we propose a novel method SAP-DIFF that leverages diffusion models to generate adversarial patches via semantic perturbations in the latent space rather than direct pixel manipulation. We introduce an attention disruption mechanism to generate features unrelated to the original face, facilitating the creation of adversarial samples and a directional loss function to guide perturbations toward the target identity feature space, thereby enhancing attack effectiveness and efficiency. Extensive experiments on popular FR models and datasets demonstrate that our method outperforms state-of-the-art approaches, achieving an average attack success rate improvement of 45.66% (all exceeding 40%), and a reduction in the number of queries by about 40% compared to the SOTA approach

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