Content-Aware Selective Encryption for H.265/HEVC Using Deep Hashing Network and Steganography


연구 분야: Strategies



학회: ACM Transactions on Multimedia Computing, Communications and Applications, Volume 21, Issue 1


초록

Existing selective encryption schemes for High Efficiency Video Coding (HEVC) only focus on the encoding characteristics of syntax elements in entropy coding and lack an understanding of the video content. Consequently, a large amount of unnecessary encryption operations are utilized to protect insensitive video frames, resulting in low encryption efficiency. In this article, we propose a content-aware selective encryption scheme for H.265/HEVC, which encrypts only the groups of pictures (GOPs) containing sensitive content and thus offers high efficiency. In our scheme, a deep hashing network is first adopted to retrieve video frames to determine the content-sensitive GOPs. Then, multiple prediction and residual syntax elements in sensitive GOPs are encrypted using a keystream sequence generated by the hyper-chaotic Lorenz system. In addition, the direct current coefficient of each 4 × 4 transform block is exchanged with a pseudo-randomly selected non-zero alternating current coefficient to further offer stronger visual distortion. Finally, the sign bits used for marking each GOP-type are reversibly embedded into the encrypted syntax elements to facilitate the decoder to distinguish the GOPs that need to be decrypted. Experimental results indicate that the proposed content-aware selective encryption scheme can efficiently protect sensitive content and is robust against all common attacks. Furthermore, it outperforms other state-of-the-art HEVC selective encryption algorithms in terms of security performance.


Author Profile
Qingxin Sheng

School of Computer Science and Engineering Northeastern University Shenyang China

Andorra
Author Profile
Chong Fu

School of Computer Science and Engineering Northeastern University Shenyang China and Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China

Andorra
Author Profile
Zhaonan Lin

School of Computer Science and Engineering Northeastern University Shenyang China

Andorra

📄 논문 정보

발행 연도 2024년
인용수 0
출판 국가 New Zealand, Andorra, China
사이트 ACM
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