Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency

The success of deep neural networks (DNNs) haspromoted the widespread applications of person re-identification (ReID). However, ReID systems inherit thevulnerability of DNNs to malicious attacks of visually in-conspicuous adversarial perturbations. Detection of adver-sarial attacks is, therefore, a fundamental requirement forrobust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach toachieve this goal by checking context inconsistency, whichis suitable for any DNN-based ReID systems. Specifically,three kinds of context inconsistencies caused by adversar-ial attacks are employed to learn a detector for distinguish-ing the perturbed examples, i.e., a) the embedding distancesbetween a perturbed query person image and its top-K re-trievals are generally larger than those between a benignquery image and its top-K retrievals, b) the embedding dis-tances among the top-K retrievals of a perturbed query im-age are larger than those of a benign query image, c) thetop-K retrievals of a benign query image obtained with mul-tiple expert ReID models tend to be consistent, which isnot preserved when attacks are present. Extensive exper-iments on the Market1501 and DukeMTMC-ReID datasetsshow that, as the first adversarial attack detection approachfor ReID,MEAADeffectively detects various adversarial at-tacks and achieves high ROC-AUC (over 97.5%).

Learning Person Re-identification Models from Videos with Weak Supervision

Most person re-identification methods, being supervised techniques, suffer from the burden of massive annotation requirement. Unsupervised methods overcome this need for labeled data, but perform poorly compared to the supervised alternatives. In order to cope with this issue, we introduce the problem of learning person re-identification models from videos with weak supervision. The weak nature of the supervision arises from the requirement of video-level labels, i.e. person identities who appear in the video, in contrast to the more precise framelevel annotations. Towards this goal, we propose a multiple instance attention learning framework for person re-identification using such video-level labels. Specifically, we first cast the video person re-identification task into a multiple instance learning setting, in which person images in a video are collected into a bag. The relations between videos with similar labels can be utilized to identify persons, on top of that, we introduce a co-person attention mechanism which mines the similarity correlations between videos with person identities in common. The attention weights are obtained based on all person images instead of person tracklets in a video, making our learned model less affected by noisy annotations. Extensive experiments demonstrate the superiority of the proposed method over the related methods on two weakly labeled person re-identification datasets.