Combining Two Adversarial Attacks Against Person Re-Identification Systems

• URL: http://arxiv.org/abs/2309.13763v1
• Date: Sun, 24 Sep 2023 22:22:29 GMT
• Title: Combining Two Adversarial Attacks Against Person Re-Identification Systems
• Authors: Eduardo de O. Andrade, Igor Garcia Ballhausen Sampaio, Joris Gu\'erin and Jos\'e Viterbo
• Abstract summary: We focus on adversarial attacks on Re-ID systems, which can be a critical threat to the performance of these systems. We combine the use of two types of adversarial attacks, P-FGSM and Deep Mis-Ranking, applied to two popular Re-ID models. The best result demonstrates a decrease of 3.36% in the Rank-10 metric for ReID applied to CUHK03.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The field of Person Re-Identification (Re-ID) has received much attention recently, driven by the progress of deep neural networks, especially for image classification. The problem of Re-ID consists in identifying individuals through images captured by surveillance cameras in different scenarios. Governments and companies are investing a lot of time and money in Re-ID systems for use in public safety and identifying missing persons. However, several challenges remain for successfully implementing Re-ID, such as occlusions and light reflections in people's images. In this work, we focus on adversarial attacks on Re-ID systems, which can be a critical threat to the performance of these systems. In particular, we explore the combination of adversarial attacks against Re-ID models, trying to strengthen the decrease in the classification results. We conduct our experiments on three datasets: DukeMTMC-ReID, Market-1501, and CUHK03. We combine the use of two types of adversarial attacks, P-FGSM and Deep Mis-Ranking, applied to two popular Re-ID models: IDE (ResNet-50) and AlignedReID. The best result demonstrates a decrease of 3.36% in the Rank-10 metric for AlignedReID applied to CUHK03. We also try to use Dropout during the inference as a defense method.

Related papers

• Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training [51.87027943520492]
  We present a novel paradigm Diffusion-ReID to efficiently augment and generate diverse images based on known identities. Benefiting from our proposed paradigm, we first create a new large-scale person Re-ID dataset Diff-Person, which consists of over 777K images from 5,183 identities.
  arXiv  Detail & Related papers  (2024-06-10T06:26:03Z)
• Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification [36.91680117072686]
  We propose Erasing, Transforming, and Noising Defense Network (ETNDNet) to solve occluded person re-ID. In the proposed ETNDNet, we randomly erase the feature map to create an adversarial representation with incomplete information. Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians.
  arXiv  Detail & Related papers  (2023-07-14T06:42:21Z)
• Learning Invariance from Generated Variance for Unsupervised Person Re-identification [15.096776375794356]
  We propose to replace traditional data augmentation with a generative adversarial network (GAN) A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.
  arXiv  Detail & Related papers  (2023-01-02T15:40:14Z)
• Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-identification [31.065557919305892]
  Unsupervised person re-identification (Re-ID) has received increasing research attention. Most clustering-based methods take each cluster as a pseudo identity class. We propose to split each single cluster into multiple proxies according to camera views.
  arXiv  Detail & Related papers  (2022-01-15T10:12:03Z)
• Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency [47.719533482898306]
  We propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to detect malicious attacks on person re-identification (ReID) systems. As the first adversarial attack detection approach for ReID,MEAADeffectively detects various adversarial at-tacks and achieves high ROC-AUC (over 97.5%).
  arXiv  Detail & Related papers  (2021-08-23T01:59:09Z)
• Camera-aware Proxies for Unsupervised Person Re-Identification [60.26031011794513]
  This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. We propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model.
  arXiv  Detail & Related papers  (2020-12-19T12:37:04Z)
• Unsupervised Pre-training for Person Re-identification [90.98552221699508]
  We present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" We make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation.
  arXiv  Detail & Related papers  (2020-12-07T14:48:26Z)
• Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking [83.48804199140758]
  We propose a learning-to-mis-rank formulation to perturb the ranking of the system output. We also perform a back-box attack by developing a novel multi-stage network architecture. Our method can control the number of malicious pixels by using differentiable multi-shot sampling.
  arXiv  Detail & Related papers  (2020-04-08T18:48:29Z)
• Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond [59.149653740463435]
  Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected. We propose a novel generative adversarial network to address cross-resolution person re-ID.
  arXiv  Detail & Related papers  (2020-02-19T07:21:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.