标题： 针对深度学习人脸检测的后门攻击 显示英文标题

标题： Backdoor Attacks on Deep Learning Face Detection

摘要： 面部识别系统在非受控环境中运行时，在不同的条件下捕获图像，例如不一致的光照或多样的面部姿态。 这些挑战需要包含一个面部检测模块，该模块回归边界框和关键点坐标以进行正确的面部对齐。 本文展示了针对面部检测的物体生成攻击的有效性，称为面部生成攻击，并首次演示了一种关键点偏移攻击，该攻击对由面部检测器执行的坐标回归任务进行后门攻击。 然后我们提供了针对这些漏洞的缓解措施。 显示英文摘要

摘要： Face Recognition Systems that operate in unconstrained environments capture images under varying conditions,such as inconsistent lighting, or diverse face poses. These challenges require including a Face Detection module that regresses bounding boxes and landmark coordinates for proper Face Alignment. This paper shows the effectiveness of Object Generation Attacks on Face Detection, dubbed Face Generation Attacks, and demonstrates for the first time a Landmark Shift Attack that backdoors the coordinate regression task performed by face detectors. We then offer mitigations against these vulnerabilities.