Existing H.265/HEVC video steganalysis research mainly focuses on detecting the steganography based on motion vectors, intra prediction modes, and transform coefficients. However, there is currently no effective steganalysis method capable of detecting steganography based on Coding Unit (CU) block structure. To address this issue, we propose, for the first time, a H.265/HEVC video steganalysis algorithm based on CU block structure gradients and intra prediction mode mapping. The proposed method first constructs a new gradient map to explicitly describe changes in CU block structure, and combines it with a block level mapping representation of IPM. It can jointly model the structural perturbations introduced by steganography based on CU block structure. Then, we design a novel steganalysis network called GradIPMFormer, whose core innovation is an integrated architecture that combines convolutional local embedding with Transformer-based token modeling to jointly capture local CU boundary perturbations and long-range cross-CU structural dependencies, thereby effectively enhancing the capability to perceive CU block structure embedding. Experimental results show that under different quantization parameters and resolution settings, the proposed method consistently achieves superior detection performance across multiple steganography methods based on CU block structure. This study provides a new CU block structure steganalysis paradigm for H.265/HEVC and has significant research value for covert communication security detection.

翻译：现有H.265/HEVC视频隐写分析研究主要聚焦于检测基于运动矢量、帧内预测模式和变换系数的隐写，但目前尚无有效方法能够检测基于编码单元（CU）块结构的隐写。针对该问题，我们首次提出一种基于CU块结构梯度和帧内预测模式映射的H.265/HEVC视频隐写分析算法。该方法首先构建新型梯度图以显式描述CU块结构变化，并将其与IPM的块级映射表示相结合，从而联合建模基于CU块结构的隐写所引入的结构扰动。随后设计了一种名为GradIPMFormer的新型隐写分析网络，其核心创新在于集成架构：通过卷积局部嵌入与基于Transformer的标记建模协同工作，可联合捕捉局部CU边界扰动与远距离跨CU结构依赖关系，从而有效增强对CU块结构嵌入的感知能力。实验结果表明，在不同量化参数与分辨率设置下，该方法对多种基于CU块结构的隐写方法均能持续取得更优检测性能。本研究为H.265/HEVC提供了全新的CU块结构隐写分析范式，对隐蔽通信安全检测具有重要研究价值。