AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
For users concerned about surveillance, AuraMask addresses the trade-off between privacy and self-presentation by creating filters that are both effective and aesthetically acceptable.
AuraMask produces 40 aesthetic anti-facial recognition filters that match or exceed prior methods' adversarial effectiveness against open-source models, and achieve significantly higher user acceptance in a 630-participant study.
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.