AuraMask：一种可扩展的开发美观反面部识别图像滤镜的管道
AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
AI总结：
本文提出AuraMask，一种生成有效且美观的反面部识别图像滤镜的方法，通过40个仿Instagram滤镜的实例，验证其在对抗有效性与用户接受度上的优势。
摘要：
反面部识别（AFR）图像滤镜通过微妙的方式改变图像，使人类难以察觉但对计算机视觉却极具破坏性。然而，尽管这些技术被广泛用于对抗监控，用户却很少实际使用它们——因为这些“微妙”的修改足以引起用户的自我呈现冲突。为了解决这一挑战，我们提出了AuraMask：一种新颖的创建AFR滤镜的方法，兼具对抗有效性与美观性。使用AuraMask，我们生成了40个“美观”的滤镜，仿照流行的“一键式”Instagram图像滤镜。我们证明AuraMask滤镜在对抗有效性方面不低于先前方法，针对开源面部识别模型。此外，在受控的在线用户研究（N=630）中，我们确认这些滤镜比先前方法显著提高了用户接受度。最后，我们向社区提供我们的AFR管道，以加速对抗有效且美观保护的研究。
英文摘要：
Anti-facial recognition (AFR) image filters alter images in ways that are subtle to people but blinding to computer vision. Yet, despite widespread interest in these technologies to subvert surveillance, users rarely use them in practice -- because the ``subtle'' alterations are visible enough to conflict with users' self-presentation goals. To address this challenge, we propose AuraMask: a novel approach to creating AFR filters that are both adversarially effective and aesthetically acceptable. Using AuraMask, we produce 40 ``aesthetic'' filters that emulate popular ``one-click'' Instagram image filters. We show that AuraMask filters meet or exceed the adversarial effectiveness of prior methods against open-source facial recognition models. Moreover, in a controlled online user study ($N=630$) we confirm these filters achieve significantly higher user acceptance than prior methods. Lastly, we provide our AFR pipeline to the community for accelerated research in adversarially effective and aesthetically acceptable protections.
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。
