论文标题
Spact：自制的隐私保护行动识别
SPAct: Self-supervised Privacy Preservation for Action Recognition
论文作者
论文摘要
视觉私人信息泄漏是一个新兴的关键问题，用于快速增长的视频理解（例如活动识别）的应用。现有的减轻行动识别隐私泄漏的方法需要隐私标签以及视频数据集中的动作标签。但是，对隐私标签的视频数据集的注释帧是不可行的。自我监督学习（SSL）的最新发展释放了未标记数据的未开发潜力。我们第一次提出了一个新颖的培训框架，该框架以自我监督的方式从输入视频中删除隐私信息，而无需隐私标签。我们的培训框架由三个主要组成部分组成：匿名功能，自我监督的隐私拆除分支和行动识别分支。我们使用最小值优化策略来训练框架，以最大程度地减少动作识别成本功能，并通过对比的自我监督损失最大化隐私成本功能。采用现有的已知行动和隐私属性协议，我们的框架为现有的最新监督方法实现了竞争性的行动私人权衡权衡。此外，我们引入了一项新协议，以评估对新颖行动和隐私属性的匿名函数的概括，并表明我们的自我监督框架的表现优于现有的监督方法。代码可用：https：//github.com/daveishan/pact
Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. However, annotating frames of video dataset for privacy labels is not feasible. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. For the first time, we present a novel training framework which removes privacy information from input video in a self-supervised manner without requiring privacy labels. Our training framework consists of three main components: anonymization function, self-supervised privacy removal branch, and action recognition branch. We train our framework using a minimax optimization strategy to minimize the action recognition cost function and maximize the privacy cost function through a contrastive self-supervised loss. Employing existing protocols of known-action and privacy attributes, our framework achieves a competitive action-privacy trade-off to the existing state-of-the-art supervised methods. In addition, we introduce a new protocol to evaluate the generalization of learned the anonymization function to novel-action and privacy attributes and show that our self-supervised framework outperforms existing supervised methods. Code available at: https://github.com/DAVEISHAN/SPAct
