论文标题
私人：认识到保护隐私镜头的人类行为
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens
论文作者
论文摘要
数码相机的加速使用引起了人们对隐私和安全性的日益关注，尤其是在诸如行动识别之类的应用程序中。在本文中，我们提出了一个优化框架，以沿着人类行动识别管道提供强大的视觉隐私保护。我们的框架参数化了相机镜头，以成功地降低视频的质量，以抑制隐私属性并防止对抗性攻击，同时保持相关功能以识别活动识别。我们通过广泛的模拟和硬件实验来验证我们的方法。
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.
