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%.

该文研究了面部识别系统，并提出了可以应对这种系统的对抗滤镜，实验表明这种滤镜可以有效的通过包含面部检测和大规模数据库的工业流水线，同时发布了易于使用的 Web 工具来降低 Amazon Rekognition 和 Microsoft Azure Face Recognition API 的准确性。