Abstract
While the rapid development of facial recognition algorithms has enabled numerous beneficial applications, their widespread deployment has raised significant concerns about the risks of mass surveillance and threats to individual privacy. In this paper, we introduce \textit{Adversarial Camouflage} as a novel solution for protecting users' privacy. This approach is designed to be efficient and simple to reproduce for users in the physical world. The algorithm starts by defining a low-dimensional pattern space parameterized by color, shape, and angle. Optimized patterns, once found, are projected onto semantically valid facial regions for evaluation. Our method maximizes recognition error across multiple architectures, ensuring high cross-model transferability even against black-box systems. It significantly degrades the performance of all tested state-of-the-art face recognition models during simulations and demonstrates promising results in real-world human experiments, while revealing differences in model robustness and evidence of attack transferability across architectures.
Abstract (translated)
尽管人脸识别算法的快速发展催生了众多有益应用，但其大规模部署也引发了关于大规模监控风险和个人隐私威胁的重大担忧。本文提出\textit{对抗性伪装}作为保护用户隐私的新方案。该方法设计高效且便于物理世界用户复现。算法首先定义由颜色、形状和角度参数化的低维模式空间，优化后的模式会被投影至语义有效的人脸区域进行评估。我们的方法能在多种架构上最大化识别错误率，确保即使面对黑盒系统也具有高跨模型迁移性。在模拟测试中，该方法显著降低了所有 tested 最先进人脸识别模型的性能，并在真实人体实验中展现出前景性结果，同时揭示了不同模型鲁棒性的差异及跨架构攻击迁移性的证据。
URL
https://arxiv.org/abs/2603.21867
