SPAct: Self-supervised Privacy Preservation for Action Recognition

Visual private information leakage is an emerging key is-sue 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 la-bels 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 in-put video in a self-supervised manner without requiring pri-vacy labels. Our training framework consists of three main components: anonymization function, self-supervised pri-vacy removal branch, and action recognition branch. We train our framework using a minimax optimization strategy to minimize the action recognition cost function and max-imize 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 at-tributes and show that our self-supervised framework out-performs existing supervised methods. Code available at: https://github.com/DAVEISHAN/SPAct