对抗性伪装
Adversarial Camouflage
AI总结：
本文提出对抗性伪装方法，通过在面部区域投影优化图案，提升隐私保护效果，降低面部识别模型性能，验证了攻击的跨模型转移性。
摘要：
尽管面部识别算法的快速发展带来了诸多有益应用，但其广泛应用引发了对大规模监控和个体隐私威胁的担忧。本文提出对抗性伪装作为保护用户隐私的新解决方案。该方法旨在为物理世界中的用户提供高效且易于复现的保护。算法首先定义由颜色、形状和角度参数化的低维模式空间。优化后的图案一旦找到，就会投射到语义有效的面部区域进行评估。我们的方法在多个架构上最大化识别错误，确保即使在对抗黑盒系统时也具有高跨模型转移性。在模拟测试中，该方法显著降低了所有测试的最新面部识别模型性能，并在现实世界的人类实验中表现出有希望的结果，同时揭示了模型鲁棒性和不同架构间攻击转移性的差异。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
