Prompt-Segmentation Meets ROI Encryption: Fine-Grained Video Protection Based on Diffusion Isolation

Abstract

Region-of-interest (ROI) selective encryption in H.265/HEVC aims to protect sensitive content while preserving the utility of non-ROI regions. Existing coding-unit (CU)-level approaches still suffer from substantial encryption redundancy for two reasons. First, existing approaches map object-detection bounding boxes to CUs, producing regular rectangular regions, which poorly match the shape of irregular ROI boundaries, such as facial contours. Second, their coding-tree-unit (CTU)-based diffusion isolation expands the protected footprint in fixed $64\times64$ blocks, causing encryption-induced distortion to spread over a large number of non-ROI pixels. To address the aforementioned shortcomings, we propose a prompt segmentation-guided CU-level ROI encryption algorithm, which mainly consists of two core components. The first component is a prompt segmentation-guided fine-grained ROI encryption pipeline. It maps pixel-level ROI masks to CUs according to an ROI-overlap threshold, refines boundary CUs to $8\times8$ granularity, and applies format-compliant encryption to multiple syntax elements in the mapped ROI CUs. The second is a dual diffusion isolation strategy to address the encryption diffusion problem. For spatial isolation, the encoder refines non-ROI boundary regions into $8\times8$ CUs and uses PCM mode to bypass intra-frame prediction. For temporal isolation, it removes motion-vector candidates whose reference blocks overlap ROI CUs. Experimental results demonstrate that the proposed method generates encryption regions that more closely match irregular ROI boundaries, effectively confines distortion to the ROI.