Name
Title | ||
|---|---|---|
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency. |
Abstract | ||
|---|---|---|
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%). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCV48922.2021.01482 | ICCV |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xueping Wang | 1 | 1 | 2.76 |
| Shasha Li | 2 | 0 | 0.34 |
| Min Liu | 3 | 5 | 2.80 |
| Yaonan Wang | 4 | 0 | 0.34 |
| Amit K. Roy Chowdhury | 5 | 1153 | 73.96 |