HEVC Video Steganalysis Based on PU Maps and Multi-Scale Convolutional Residual Network

被引:7
作者
Dai, Haojun [1 ]
Wang, Rangding [1 ]
Xu, Dawen [2 ]
He, Songhan [1 ]
Yang, Lin [1 ]
机构
[1] Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Peoples R China
[2] Ningbo Univ Technol, Sch Cyber Sci & Engn, Ningbo 315211, Peoples R China
基金
中国国家自然科学基金;
关键词
Steganography; Feature extraction; Encoding; Streaming media; Convolutional codes; Residual neural networks; Quantization (signal); Video steganalysis; convolutional neural network; HEVC; PU partition mode; DATA HIDING ALGORITHM; STEGANOGRAPHY; CNN;
D O I
10.1109/TCSVT.2023.3309861
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
HEVC (High Efficiency Video Coding) provides abundant embedding carriers for video steganography, leading to rapid development in the field of video steganography while increasing the urgent demand for video steganalysis. However, existing steganalysis methods against PU (prediction unit) based steganography primarily use the extraction of video statistical features, which ignore the potential information of each frame and fail to effectively detect different PU-based steganography methods. In this paper, a video steganalysis method based on PU maps and multi-scale convolutional residual network is proposed. Firstly, the effects of PU-based steganography on the spatial domain and the compressed domain are analyzed. It is observed that steganography has less impact on the spatial domain, whereas it significantly disrupts the connection between PU blocks in the compressed domain, leaving distinct steganographic traces. Consequently, the PU partition modes containing local connections are introduced to generate PU maps for steganalysis. Secondly, a video steganalysis network called PUSN (Prediction Unit Steganalysis Network) is constructed. The network takes PU maps as input and consists of three parts: feature extraction, feature representation, and binary classification. Additionally, a multi-scale module is proposed to enhance the detection performance. Finally, the detection result of the steganographic video is obtained by the voting mechanism. The experimental results show that compared with the existing steganalysis methods, the proposed method could effectively detect multiple PU-based steganography methods and achieve higher detection accuracy across various embedding rates.
引用
收藏
页码:2663 / 2676
页数:14
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