Video has become increasingly widespread in information transmission and daily communication. However, video data often contain sensitive privacy information, which leads to privacy concerns. To address these concerns, video encryption has become one of the most effective privacy protection methods. Current efforts in video encryption focus on block-level or frame-level encryption, failing to provide fine-grained privacy protection. In response, we propose a personalized pixel-level video encryption approach for privacy protection. Our approach operates before encoding and is robust to lossy compression. It applies a quadruple encryption algorithm directly to the pixels within privacy-sensitive areas segmented by instance segmentation, achieving fine-grained, pixel-level encryption. Different encryption keys are managed using Attribute-Based Encryption (ABE) technology, which enables personalized access control. This approach assigns different permissions based on user identity, enabling multi-level access to encrypted video content.We deploy our approach across different devices and perform extensive experimental evaluations. Experimental results show that on different devices, the single-frame encryption overhead of our approach for videos of various resolutions consistently remains below 0.12 seconds, with the encrypted area achieving an average PSNR of 8.50 dB and an average SSIM of 0.079. When using a key with one bit difference from the correct key for decryption, the average number of pixels changing rate (NPCR) of the test sequence is 99.59% and the average unified average change intensity (UACI) is 32.40%. This shows that unauthorized users cannot obtain valid information when decrypting with an incorrect key.

Object distance estimation is a critical task in autonomous driving and has received increasing attention in recent years. Existing methods rely on either geometric cues, which typically reduce structures to coarse attributes like 2D box dimensions, or visual cues that degrade under occlusion and truncation. In this work, we propose a geometry-enhanced framework that augments visual features with a more stable and descriptive geometric attribute—Patch-To-Center Geometry (PCG)—which encodes the spatial distance between local object patches and the 3D object center projection of the object to improve distance estimation. Specifically, each object is divided into multiple patches, and a dedicated attention module is employed to learn the pixel-wise spatial offsets from each patch to the projected center, serving as an auxiliary supervision signal. Additionally, a head token is introduced to aggregate global information from all patches for final distance prediction. Extensive experiments on the KITTI and nuScenes datasets demonstrate that our method outperforms existing approaches when limiting the minimum depth threshold.