Model-aware privacy-preserving with start trigger method for person re-identification

Abstract

Person Re-identification (ReID) could search for the same pedestrian from non-overlapping cameras, which completes the pedestrian location and search purpose. However, the process contains much sensitive pedestrian information and raises serious privacy problems. Conventional methods mainly remove identity-related features from pedestrian images to alleviate the privacy issue. Unfortunately, these strategies cause pedestrian information loss and poor data utility. In the paper, we propose a novel Model-Aware Privacy-Preserving with Start Trigger (MPST) method, which not only prevents personal identity for third parties but also achieves accurate pedestrian location for authorized organizations. The core idea is that authorized organizations obtain the start trigger to activate the ReID model that has the ability to search for target pedestrians, while third parties (i.e., hackers) cannot employ the ReID model to complete the pedestrian matching task without the start trigger. To this end, we develop a universal adversarial algorithm to learn an ingenious start trigger for the person ReID system. Afterwards, we further design a model-aware training strategy to facilitate our deep model to perceive issued instructions by synthetically utilizing the start trigger and original pedestrian samples. As a result, we successfully install an activate button to change the ReID model state for deciding whether the deep model has the ability to search for pedestrians or not. Abundant experiments demonstrate that the proposed MPST is effective for pedestrian identity anonymization. Our study achieves superior performance for authorized organizations and completes the privacy protection goal.