AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 119 references that are in the map. First posted on arXiv 13 May 2026.
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30 of 119 references.
References · 30
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18 more
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