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%
翻译:私人公司、政府机构和消费者服务承包商以及大规模监视方案都越来越多地部署面部识别系统。这些系统通常通过为用户图像收集社交媒体信息来建立。建议绕过面部识别系统进行反扰动。但是,现有的方法在全系统和商业API上都失败。我们开发了自己的对抗过滤器,该过滤器负责整个图像处理管道,对包括面对面检测和大规模数据库在内的工业级管道具有明显效力。此外,我们发行了一个易于使用的网络工具,大大降低了亚马逊Rekognition和微软Azure面对面识别API的准确性,将每种信息的准确性降低到1%以下。