arXiv:2604.08047eess.IVcs.MM2026-04

提出基于编码单元的细粒度视频加密方法,精准保护敏感区域。

A H.265/HEVC Fine-Grained ROI Video Encryption Algorithm Based on Coding Unit and Prompt Segmentation

  • 用提示分割技术精确定位8×8编码单元级别的感兴趣区域。
  • 通过多语法元素扰动实现高精度区域内像素的有效加密。
  • 结合PCM模式与运动矢量限制,消除加密扩散伪影,适合医疗军事场景。

基于H.265/HEVC的感兴趣区域(ROI)视频选择性加密技术通过扰动目标区域相关的语法元素来保护视频中的敏感内容。然而,现有方法通常以较大尺寸的Tile作为最小加密单元,存在加密区域不精确、精度低的问题,难以应用于医疗、军事和遥感等敏感领域。为此,本文提出一种基于编码单元(CUs)和提示分割的细粒度ROI视频选择性加密算法。首先,提出一种新的提示分割驱动的ROI映射方法,可将ROI精确映射至8×8大小的编码单元级别,显著提升加密区域精度;其次,设计基于多个语法元素的选择性加密方案,对高精度ROI内的语法元素进行扰动,有效保障安全;最后,提出基于脉冲编码调制(PCM)模式和运动矢量(MV)限制的扩散隔离策略,通过对受影响的编码单元应用该策略,抑制预测过程中的加密扩散问题。上述三类策略打破了传统使用Tile的固有机制,将ROI加密精细度提升至最小8×8编码单元级别。实验结果表明,该算法能准确分割ROI区域,有效扰动区域内像素,并消除加密引入的扩散伪影,具有在医学影像、军事监控和偏远地区等场景中的应用潜力。

原文摘要 · Abstract (English)

ROI (Region of Interest) video selective encryption based on H.265/HEVC is a technology that protects the sensitive regions of videos by perturbing the syntax elements associated with target areas. However, existing methods typically adopt Tile (with a relatively large size) as the minimum encryption unit, which suffers from problems such as inaccurate encryption regions and low encryption precision. This low-precision encryption makes them difficult to apply in sensitive fields such as medicine, military, and remote sensing. In order to address the aforementioned problem, this paper proposes a fine-grained ROI video selective encryption algorithm based on Coding Units (CUs) and prompt segmentation. First, to achieve a more precise ROI acquisition, we present a novel ROI mapping approach based on prompt segmentation. This approach enables precise mapping of ROIs to small $8\times8$ CU levels, significantly enhancing the precision of encrypted regions. Second, we propose a selective encryption scheme based on multiple syntax elements, which distorts syntax elements within high-precision ROI to effectively safeguard ROI security. Finally, we design a diffusion isolation based on Pulse Code Modulation (PCM) mode and MV restriction, applying PCM mode and MV restriction strategy to the affected CU to address encryption diffusion during prediction. The above three strategies break the inherent mechanism of using Tiles in existing ROI encryption and push the fine-grained level of ROI video encryption to the minimum $8\times8$ CU precision. The experimental results demonstrate that the proposed algorithm can accurately segment ROI regions, effectively perturb pixels within these regions, and eliminate the diffusion artifacts introduced by encryption. The method exhibits great potential for application in medical imaging, military surveillance, and remote areas.

视频加密细粒度加密医疗影像编码单元

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