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LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition

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arxiv 2101.07922 v2 pith:NLPS6QTE submitted 2021-01-20 cs.CV cs.CRcs.LG

Valeriia Cherepanova , Micah Goldblum , Harrison Foley , Shiyuan Duan , John Dickerson , Gavin Taylor , Tom Goldstein This is my paper · ORCID
classification cs.CVcs.CRcs.LG
keywords recognitionsystemsadversarialfacialaccuracyfacemediasocial
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Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These systems are typically built by scraping social media profiles for user images. Adversarial perturbations have been proposed for bypassing facial recognition systems. However, existing methods fail on full-scale systems and commercial APIs. We develop our own adversarial filter that accounts for the entire image processing pipeline and is demonstrably effective against industrial-grade pipelines that include face detection and large scale databases. Additionally, we release an easy-to-use webtool that significantly degrades the accuracy of Amazon Rekognition and the Microsoft Azure Face Recognition API, reducing the accuracy of each to below 1%.

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Cited by 3 Pith papers

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    Injecting inaudible low-frequency adversarial perturbations into loudspeaker audio can block vibration-based side-channel speech eavesdropping with over 97% classifier protection while preserving perceived audio quality.

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    Data augmentation restores learnability of unlearnable private images, and ARMOR's surrogate-based noise generation reduces this leakage across architectures.

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