  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%.
