让反人脸识别滤镜既有效又好看，像Instagram滤镜一样自然。
AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
- 用可扩展管道生成视觉美观的反人脸识别滤镜
- 40个滤镜在630人实验中接受度显著更高
- 兼顾对抗有效性与用户审美，适合隐私保护研究者
反人脸识别（AFR）图像滤镜通过人眼难以察觉的修改，使计算机视觉系统失效。然而，由于现有方法的修改仍可见，用户常因担心影响自我形象展示而放弃使用。为此，我们提出AuraMask：一种新型生成美学化AFR滤镜的管道。利用该方法，我们生成了40种模仿流行“一键”Instagram滤镜效果的滤镜。实验表明，这些滤镜在对抗开源人脸检测模型时表现不逊于或优于先前方法。在一项受控在线用户研究中（N=630），验证了其显著更高的用户接受度。最后，我们向社区开放该AFR开发管道，以推动兼具对抗性与美学性的隐私保护研究。
原文摘要 · Abstract (English)
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.
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