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Title: LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
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%.  more » « less
Award ID(s):
1846237 1852352
NSF-PAR ID:
10315226
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the International Conference on Learning Representations (ICLR)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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