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Facial recognition systems are increasingly deployed by private corporations,government agencies, and contractors for consumer services and masssurveillance programs alike. These systems are typically built by scrapingsocial media profiles for user images. Adversarial perturbations have beenproposed for bypassing facial recognition systems. However, existing methodsfail on full-scale systems and commercial APIs. We develop our own adversarialfilter that accounts for the entire image processing pipeline and isdemonstrably effective against industrial-grade pipelines that include facedetection and large scale databases. Additionally, we release an easy-to-usewebtool that significantly degrades the accuracy of Amazon Rekognition and theMicrosoft Azure Face Recognition API, reducing the accuracy of each to below1%.

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