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

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.