AI summaryⓘ
The authors propose a new way to protect important parts of videos by encrypting smaller sections called Coding Units (CUs) instead of larger tiles, allowing for more precise encryption. They use a method called prompt segmentation to accurately find these important regions and then scramble specific video data elements within those regions to keep them secure. To avoid problems caused by encryption spreading to nearby video parts, they apply special techniques involving PCM mode and motion vector restrictions. Their approach improves the accuracy and effectiveness of protecting sensitive video areas, making it useful for fields like medicine and military. Experiments show that their method works well without causing unwanted visual issues.
ROI (Region of Interest)Selective EncryptionH.265/HEVCCoding Unit (CU)Prompt SegmentationSyntax ElementsPulse Code Modulation (PCM)Motion Vector (MV)Encryption Diffusion
Authors
Xiang Zhang, Haoyan Lu, Ziqiang Li, Ziwen He, Zhenshan Tan, Fei Peng, Zhangjie Fu
Abstract
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.