The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition. In this paper, we propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline. Our framework parameterizes the camera lens to successfully degrade the quality of the videos to inhibit privacy attributes and protect against adversarial attacks while maintaining relevant features for activity recognition. We validate our approach with extensive simulations and hardware experiments.

翻译：加速使用数码相机使人们越来越关注隐私和安全,特别是在行动识别等应用程序中的隐私和安全。在本文件中,我们提议了一个最优化的框架,在人类行动识别管道沿线提供强健的视觉隐私保护。我们的框架参数将相机镜头作为参数,以便成功地降低视频质量,抑制隐私属性和防范对抗性攻击,同时保持活动识别的相关特征。我们用广泛的模拟和硬件实验来验证我们的做法。