# Cross-Modality Perturbation Synergy Attack for Person Re-identification

arXiv: https://arxiv.org/abs/2401.10090 · HTML: https://mlsanity.com/paper/2401.10090/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 55 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 26 of 26)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2007.09314/) (2020)
- [Functional Adversarial Attacks](https://mlsanity.com/paper/1906.00001/) (2019)
- [Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2303.14481/) (2023)
- [Camera Style Adaptation for Person Re-identification](https://mlsanity.com/paper/1711.10295/) (2017)
- [Person Re-identification Method Based on Color Attack and Joint Defence](https://mlsanity.com/paper/2111.09571/) (2021)
- [Learning to Adapt Invariance in Memory for Person Re-identification](https://mlsanity.com/paper/1908.00485/) (2019)
- [Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking](https://mlsanity.com/paper/2004.04199/) (2020)
- [Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method](https://mlsanity.com/paper/2101.08533/) (2021)
- [3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation](https://mlsanity.com/paper/2308.16632/) (2023)
- [Universal Perturbation Attack Against Image Retrieval](https://mlsanity.com/paper/1812.00552/) (2018)
- [Dynamic Prototype Mask for Occluded Person Re-Identification](https://mlsanity.com/paper/2207.09046/) (2022)
- [UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity](https://mlsanity.com/paper/2312.03441/) (2023)
- [Lifelong Person Re-Identification via Knowledge Refreshing and Consolidation](https://mlsanity.com/paper/2211.16201/) (2022)
- [Towards Local Visual Modeling for Image Captioning](https://mlsanity.com/paper/2302.06098/) (2023)
- [MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2303.14626/) (2023)

## Built on by (most cited first, 30 of 45)

- [Towards Universal Modal Tracking with Online Dense Temporal Token Learning](https://mlsanity.com/paper/2507.20177/) (2025)
- [IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification](https://mlsanity.com/paper/2503.10324/) (2025)
- [Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift](https://mlsanity.com/paper/2410.09836/) (2024)
- [Exploring Color Invariance through Image-Level Ensemble Learning](https://mlsanity.com/paper/2401.10512/) (2024)
- [Beyond Augmentation: Empowering Model Robustness under Extreme Capture Environments](https://mlsanity.com/paper/2407.13640/) (2024)
- [Phy124: Fast Physics-Driven 4D Content Generation from a Single Image](https://mlsanity.com/paper/2409.07179/) (2024)
- [Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking](https://mlsanity.com/paper/2407.13646/) (2024)
- [RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification](https://mlsanity.com/paper/2411.01225/) (2024)
- [Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2402.19026/) (2024)
- [Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization](https://mlsanity.com/paper/2409.17977/) (2024)
- [Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift](https://mlsanity.com/paper/2407.13700/) (2024)
- [LLaVA-ReID: Selective Multi-image Questioner for Interactive Person Re-Identification](https://mlsanity.com/paper/2504.10174/) (2025)
- [Cross-video Identity Correlating for Person Re-identification Pre-training](https://mlsanity.com/paper/2409.18569/) (2024)
- [Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification](https://mlsanity.com/paper/2507.01884/) (2025)
- [NEXT: Multi-Grained Mixture of Experts via Text-Modulation for Multi-Modal Object Re-Identification](https://mlsanity.com/paper/2505.20001/) (2025)
- [Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models](https://mlsanity.com/paper/2408.08989/) (2024)
- [A Theory-Inspired Framework for Few-Shot Cross-Modal Sketch Person Re-Identification](https://mlsanity.com/paper/2511.18677/) (2025)
- [Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models](https://mlsanity.com/paper/2411.05056/) (2024)
- [VisionLaw: Inferring Interpretable Intrinsic Dynamics from Visual Observations via Bilevel Optimization](https://mlsanity.com/paper/2508.13792/) (2025)
- [Diffusion-Based Image Editing: An Unforeseen Adversary to Robust Invisible Watermarks](https://mlsanity.com/paper/2511.05598/) (2025)
- [Statistical Characteristic-Guided Denoising for Rapid High-Resolution Transmission Electron Microscopy Imaging](https://mlsanity.com/paper/2603.18834/) (2026)
- [SSCM: A Spatial-Semantic Consistent Model for Multi-Contrast MRI Super-Resolution](https://mlsanity.com/paper/2509.18593/) (2025)
- [Learning with Dual-level Noisy Correspondence for Multi-modal Entity Alignment](https://mlsanity.com/paper/2510.18240/) (2025)
- [Vanishing Watermarks: Diffusion-Based Image Editing Undermines Robust Invisible Watermarking](https://mlsanity.com/paper/2602.20680/) (2026)
- [Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification](https://mlsanity.com/paper/2603.19678/) (2026)
- [RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features](https://mlsanity.com/paper/2504.18556/) (2025)
- [Geometrically Regularized Transfer Learning with On-Manifold and Off-Manifold Perturbation](https://mlsanity.com/paper/2505.15191/) (2025)
- [Editing on the Generative Manifold: A Theoretical and Empirical Study of General Diffusion-Based Image Editing Trade-offs](https://mlsanity.com/paper/2603.29736/) (2026)
- [A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches](https://mlsanity.com/paper/2409.01219/) (2024)
- [Fusing Physics-Driven Strategies and Cross-Modal Adversarial Learning: Toward Multi-Domain Applications](https://mlsanity.com/paper/2412.00341/) (2024)

## Abstract (arXiv)

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. The code will be available at https://github.com/finger-monkey/cmps__attack.