提出首个压缩友好的隐私保护动作识别方法，加密视频可直接被模型识别。
CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers
- 用密钥变换模型参数，实现加密视频直接推理
- 在M-JPEG和H.264压缩下，UCF101与HMDB51上性能优于旧方法
- 适合需高压缩率且保隐私的视频分析场景
隐私保护动作识别（PPAR）使机器能在不泄露敏感视觉内容的前提下理解视频中的人类行为。现有基于加密的方法虽能提供强隐私保护并保持高识别性能，但在视频压缩时识别性能和视觉质量急剧下降，即不兼容压缩。本文提出首个压缩友好的加密方法CFE-PPAR：使用密钥加密视频后，可通过同密钥变换参数的视频变压器直接识别。实验表明，在UCF101和HMDB51数据集上，采用Motion-JPEG和H.264压缩时，该方法均优于已有方案。
原文摘要 · Abstract (English)
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.
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