CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers

TL;DR

This paper introduces CFE-PPAR, a novel encryption method for privacy-preserving action recognition that maintains high recognition accuracy even after video compression, leveraging video transformers and secret key transformations.

Contribution

CFE-PPAR is the first encryption scheme for PPAR that is compatible with video compression, enabling direct recognition without performance loss.

Findings

CFE-PPAR outperforms previous methods on UCF101 and HMDB51 datasets.

It maintains recognition accuracy under Motion-JPEG and H.264 compression.

The method enables recognition directly on encrypted videos.

Abstract

Privacy-preserving action recognition (PPAR) enables machines to understand human activities in videos without revealing sensitive visual content. Among the various strategies for PPAR, encryption-based methods achieve strong privacy protection while maintaining high recognition performance. However, these methods lead to a catastrophic decrease in recognition performance and visual quality when the encrypted videos are compressed. That is, the previous methods are not compression-friendly. To address these issues, in this paper, we propose the first compression-friendly encryption method for PPAR, called CFE-PPAR. In CFE-PPAR, videos encrypted with secret keys can be directly recognized by a video transformer, which uses parameters transformed by the same keys as those used for video encryption. In experiments, it is verified that CFE-PPAR outperforms previous methods on the UCF101 and HMDB51 datasets under Motion-JPEG and H.264 compression.