CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers
📰 ArXiv cs.AI
Learn how CFE-PPAR enables privacy-preserving action recognition in videos without compromising visual quality or recognition performance, even when compressed.
Action Steps
- Implement CFE-PPAR using video transformers to leverage compression-friendly encryption for PPAR
- Evaluate the recognition performance of CFE-PPAR on compressed videos
- Compare the visual quality of CFE-PPAR with other encryption-based methods
- Apply CFE-PPAR to real-world applications, such as smart home surveillance or healthcare monitoring
- Test the robustness of CFE-PPAR against various compression ratios and video qualities
Who Needs to Know This
Computer vision engineers and researchers working on privacy-preserving action recognition can benefit from this approach, as it enables strong privacy protection while maintaining high recognition performance.
Key Insight
💡 CFE-PPAR achieves strong privacy protection while maintaining high recognition performance, even when compressed, by leveraging video transformers and compression-friendly encryption.
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📹🔒 Introducing CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition in videos! 💡
Key Takeaways
Learn how CFE-PPAR enables privacy-preserving action recognition in videos without compromising visual quality or recognition performance, even when compressed.
Full Article
Title: CFE-PPAR: Compression-friendly encryption for privacy-preserving action recognition leveraging video transformers
Abstract:
arXiv:2605.05692v1 Announce Type: cross 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 p
Abstract:
arXiv:2605.05692v1 Announce Type: cross 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 p
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