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.

翻译：现有的H.265/HEVC视频隐写分析研究主要集中在运动矢量（MV）、帧内预测模式（IPM）或变换系数等层面的统计特征建模。相比之下，针对编码结构层面——尤其是编码单元（CU）中块级隐写行为的分析——仍处于早期阶段。作为H.265/HEVC编码决策的核心组成部分，CU划分结构常以结构变化和预测关系重组的形式表现出隐写扰动，这些扰动难以通过传统的像素域特征或模式统计进行有效刻画。为解决这一问题，本文首次从CU块级隐写分析的视角，提出了一种基于CU块结构梯度与帧内预测模式映射的H.265/HEVC视频隐写分析方法。该方法构建了CU块结构梯度图以显式描述编码单元划分的变化，并结合IPM的块级映射表示，共同建模由CU级隐写嵌入引入的结构扰动。在此基础上，我们设计了一个专为CU块隐写分析定制的Transformer网络——GradIPMFormer，从而有效增强对CU级隐写行为的感知能力。实验结果表明，在不同量化参数与分辨率设置下，所提方法在多种H.265/HEVC隐写算法中均能取得优越的检测性能，验证了从编码结构视角进行视频隐写分析的可行性与有效性。本研究为H.265/HEVC视频隐写分析提供了新的CU块级分析范式，对隐蔽通信安全检测具有重要的研究价值。