保护行车视频位置隐私，只扰动关键背景区域。
PPEDCRF: Privacy-Preserving Enhanced Dynamic CRF for Location-Privacy Protection for Sequence Videos with Minimal Detection Degradation
- 动态条件随机场追踪视频中敏感背景区域。
- 顶k检索准确率显著下降，检测精度基本不变。
- 适合自动驾驶数据共享场景的隐私保护。
自动驾驶系统采集的行车视频常用于安全审计与模型优化。即使移除显式GPS元数据，攻击者仍可通过匹配背景视觉线索（如建筑和道路布局）与大规模街景图像推断位置。本文针对基于背景检索的攻击，提出PPEDCRF框架：在仅对推断出的位置敏感背景区域注入校准扰动的同时，保留前景检测性能。该框架包含三部分：(i) 动态条件随机场，保持时序一致性以追踪敏感区域；(ii) 归一化控制惩罚（NCP），依据层级敏感度模型分配扰动强度；(iii) 保用噪声注入模块，最小化对目标检测与分割的干扰。在公开驾驶数据集上的实验表明，相比全局噪声、白噪声遮蔽及特征匿名化等基线方法，PPEDCRF显著降低位置检索成功率（如顶k准确率），同时维持优异的检测性能（如mAP与分割指标）。源码见https://github.com/mabo1215/PPEDCRF.git。
原文摘要 · Abstract (English)
Dashcam videos collected by autonomous or assisted-driving systems are increasingly shared for safety auditing and model improvement. Even when explicit GPS metadata are removed, an attacker can still infer the recording location by matching background visual cues (e.g., buildings and road layouts) against large-scale street-view imagery. This paper studies location-privacy leakage under a background-based retrieval attacker, and proposes PPEDCRF, a privacy-preserving enhanced dynamic conditional random field framework that injects calibrated perturbations only into inferred location-sensitive background regions while preserving foreground detection utility. PPEDCRF consists of three components: (i) a dynamic CRF that enforces temporal consistency to discover and track location sensitive regions across frames, (ii) a normalized control penalty (NCP) that allocates perturbation strength according to a hierarchical sensitivity model, and (iii) a utility-preserving noise injection module that minimizes interference to object detection and segmentation. Experiments on public driving datasets demonstrate that PPEDCRF significantly reduces location-retrieval attack success (e.g., Top-k retrieval accuracy) while maintaining competitive detection performance (e.g., mAP and segmentation metrics) compared with common baselines such as global noise, white-noise masking, and feature-based anonymization. The source code is in https://github.com/mabo1215/PPEDCRF.git
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