Adv-TGD is a text-guided diffusion attack that achieves 85.9% black-box ASR on four face recognition models while preserving PSNR 28.18 dB and SSIM 0.981.
Lowkey: Leveraging adversarial attacks to protect social media users from facial recognition
4 Pith papers cite this work. Polarity classification is still indexing.
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AuraMask produces 40 aesthetic anti-facial recognition filters that match or exceed prior adversarial effectiveness and achieve significantly higher user acceptance in a 630-person study.
FaceCloak learns a lightweight identity-specific cloaking mask from a single image via synthetic face generation and iterative embedding perturbation to evade multiple recognition models.
Case study of grassroots participatory design in US AI policymaking for marginalized communities, documenting engagement challenges and offering recommendations.
citing papers explorer
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Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks
Adv-TGD is a text-guided diffusion attack that achieves 85.9% black-box ASR on four face recognition models while preserving PSNR 28.18 dB and SSIM 0.981.
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AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
AuraMask produces 40 aesthetic anti-facial recognition filters that match or exceed prior adversarial effectiveness and achieve significantly higher user acceptance in a 630-person study.
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Personalized Face Privacy Protection From a Single Image
FaceCloak learns a lightweight identity-specific cloaking mask from a single image via synthetic face generation and iterative embedding perturbation to evade multiple recognition models.
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Challenges to Grassroots Organization Engagement with AI Policy
Case study of grassroots participatory design in US AI policymaking for marginalized communities, documenting engagement challenges and offering recommendations.