# Combining Two Adversarial Attacks Against Person Re-Identification Systems

arXiv: https://arxiv.org/abs/2309.13763 · HTML: https://mlsanity.com/paper/2309.13763/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [A Discriminatively Learned CNN Embedding for Person Re-identification](https://mlsanity.com/paper/1611.05666/) (2016)
- [AlignedReID: Surpassing Human-Level Performance in Person Re-Identification](https://mlsanity.com/paper/1711.08184/) (2017)
- [Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking](https://mlsanity.com/paper/2004.04199/) (2020)
- [advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns](https://mlsanity.com/paper/1908.09327/) (2019)
- [Deep Feature Learning with Relative Distance Comparison for Person Re-identification](https://mlsanity.com/paper/1512.03622/) (2015)
- [Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval](https://mlsanity.com/paper/1809.02681/) (2018)
- [Towards Practical Implementations of Person Re-Identification from Full Video Frames](https://mlsanity.com/paper/2009.01377/) (2020)
- [Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification](https://mlsanity.com/paper/1807.11042/) (2018)

## Abstract (arXiv)

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.