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This paper introduces a novel steganalysis method for H.265/HEVC video that focuses on the coding unit (CU) block structure, addressing the limitations of existing methods that primarily analyze motion vectors, intra prediction modes, or transform coefficients. The method constructs a CU block-structure gradient map to capture changes in coding-unit partitioning and combines it with a block-level mapping representation of intra prediction modes to model steganographic perturbations. A tailored Transformer network, GradIPMFormer, is designed to enhance the perception of CU-level steganographic behaviors, demonstrating superior detection performance across multiple H.265/HEVC steganographic algorithms.
H.265/HEVC video steganalysis gets a boost from a new method that spots hidden messages by analyzing the gradients in coding unit block structures, outperforming existing techniques.
Existing H.265/HEVC video steganalysis research mainly focuses on statistical feature modeling at the levels of motion vectors (MV), intra prediction modes (IPM), or transform coefficients. In contrast, studies targeting the coding-structure level - especially the analysis of block-level steganographic behaviors in Coding Units (CUs) - remain at an early stage. As a core component of H.265/HEVC coding decisions, the CU partition structure often exhibits steganographic perturbations in the form of structural changes and reorganization of prediction relationships, which are difficult to characterize effectively using traditional pixel-domain features or mode statistics. To address this issue, this paper, for the first time from the perspective of CU block-level steganalysis, proposes an H.265/HEVC video steganalysis method based on CU block-structure gradients and intra prediction mode mapping. The proposed method constructs a CU block-structure gradient map to explicitly describe changes in coding-unit partitioning, and combines it with a block-level mapping representation of IPM to jointly model the structural perturbations introduced by CU-level steganographic embedding. On this basis, we design a Transformer network, GradIPMFormer, tailored for CU-block steganalysis, thereby effectively enhancing the capability to perceive CU-level steganographic behaviors. Experimental results show that under different quantization parameters and resolution settings, the proposed method consistently achieves superior detection performance across multiple H.265/HEVC steganographic algorithms, validating the feasibility and effectiveness of conducting video steganalysis from the coding-structure perspective. This study provides a new CU block-level analysis paradigm for H.265/HEVC video steganalysis and has significant research value for covert communication security detection.
