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

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 \(4\times 4\) 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.

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Publication Details

Journal
ACM Transactions on Multimedia Computing Communications and Applications
Published
2024-10-08
DOI
https://doi.org/10.1145/3698400
Citations
1
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
Field-Weighted Citation Impact
0.25
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article

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

Zhaonan Lin, Chiu‐Wing Sham, Junxin Chen, Chong Fu et al.
1 citations
ACM Transactions on Multimedia Computing Communications and Applications
Advanced Steganography and Watermarking Techniques
0.25
article

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

Zhaonan Lin, Chiu‐Wing Sham, Junxin Chen, Chong Fu, Xingwei Wang, Qingxin Sheng
article en
1 citations

Abstract

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 \(4\times 4\) 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.

ACM Transactions on Multimedia Computing Communications and ApplicationsVol. 21(1)
University of Auckland (NZ), Dalian University of Technology (CN), Northeastern University (CN)
Openalex Percentile: Top 46%
Advanced Steganography and Watermarking Techniques
0.25
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