H.265/HEVC Video Steganalysis Based on CU Block Structure Gradients and IPM Mapping

2026-02-12

AI Summary

This work addresses the challenge that existing steganalysis methods for H.265/HEVC video struggle to effectively capture coding unit (CU)-level structural perturbations induced by steganographic embedding. To overcome this limitation, the authors propose a novel paradigm that constructs CU block-structure gradient maps and integrates them with block-level mappings derived from intra-prediction modes (IPMs) to jointly model steganography-induced structural anomalies. A dedicated Transformer architecture, termed GradIPMFormer, is designed to exploit these fused representations for detection. By moving beyond conventional pixel-domain or statistical-feature-based approaches, the proposed method achieves state-of-the-art performance across various quantization parameters and resolutions against mainstream H.265 steganographic algorithms, thereby demonstrating the efficacy and superiority of analyzing steganographic traces from the perspective of encoding structure.

Technology Category

• Machine Learning: Structured Learning
• Computer Vision: Adversarial Attacks & Robustness
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• H.265/HEVC
• video steganalysis
• Coding Unit (CU)
• block structure
• steganographic perturbations

Innovation

• CU block-structure gradient
• intra prediction mode mapping
• H.265/HEVC steganalysis
• GradIPMFormer
• coding-structure perturbation