Adversarial Perturbation

Adversarial perturbation research focuses on developing and mitigating the vulnerability of machine learning models to maliciously crafted inputs designed to cause misclassification or other errors. Current research emphasizes improving the robustness of various model architectures, including deep convolutional neural networks, vision transformers, and graph neural networks, often employing techniques like adversarial training, vector quantization, and optimal transport methods. This field is crucial for ensuring the reliability and security of AI systems across diverse applications, from image classification and face recognition to robotics and natural language processing, by identifying and addressing vulnerabilities to attacks.

Papers

October 4, 2024

October 2, 2024

MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning
Sedjro Salomon Hotegni, Sebastian Peitz
Reinforcement Learning Adversarial Attack Adversarial Example Adversarial Robustness Adversarial Perturbation Multi Objective Adversarial Feature

September 29, 2024

Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises
Anubhooti Jain, Susim Roy, Kwanit Gupta, Mayank Vatsa, Richa Singh
Adversarial Attack Deep Learning Model Adversarial Perturbation Industrial Disturbing Noise Intent Detection Adversarial Noise Kill Chaos Detection Network

September 24, 2024

Adversarial Watermarking for Face Recognition
Yuguang Yao, Anil Jain, Sijia Liu
Adversarial Attack Adversarial Perturbation Face Recognition Watermarking Method Agnostic Watermarking Adversarial Watermarking

September 21, 2024

Cloud Adversarial Example Generation for Remote Sensing Image Classification
Fei Ma, Yuqiang Feng, Fan Zhang, Yongsheng Zhou
Adversarial Attack Adversarial Example Adversarial Perturbation Remote Sensing Image Classification Adversarial Cloud

September 20, 2024

Efficient Visualization of Neural Networks with Generative Models and Adversarial Perturbations
Athanasios Karagounis
Neural Network Adversarial Attack Generative Model Adversarial Example Adversarial Perturbation Generative Network RIN Visualization

September 19, 2024

Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach
Nastaran Darabi, Dinithi Jayasuriya, Devashri Naik, Theja Tulabandhula, Amit Ranjan Trivedi
Adversarial Attack Adversarial Example Adversarial Robustness Adversarial Perturbation 3D Perception Adversarial Objective

September 17, 2024

Contextual Breach: Assessing the Robustness of Transformer-based QA Models
Asir Saadat, Nahian Ibn Asad, Md Farhan Ishmam
Native Robustness Adversarial Perturbation Adversarial Noise Contextual Integrity

September 8, 2024

2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures
Xinheng Xie, Kureha Yamaguchi, Margaux Leblanc, Simon Malzard, Varun Chhabra, Victoria Nockles, Yue Wu
Adversarial Attack Anomaly Detection Semi Supervised Adversarial Perturbation Image Data Adversarial Scenario 2 Dimensional Detection

August 29, 2024

Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Zhuohang Li, Andrew Lowy, Jing Liu, Toshiaki Koike-Akino, Kieran Parsons, Bradley Malin, Ye Wang
Machine Learning Differential Privacy Adversarial Perturbation Inference Attack Inference Privacy

August 28, 2024

Certified Causal Defense with Generalizable Robustness
Yiran Qiao, Yu Yin, Chen Chen, Jing Ma
Adversarial Attack Adversarial Perturbation Adversarial DEfense Certified Defense Causal Influence

August 26, 2024

August 24, 2024

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
Jiwei Guan, Tianyu Ding, Longbing Cao, Lei Pan, Chen Wang, Xi Zheng
Adversarial Attack Native Robustness Adversarial Robustness Vision Language Adversarial Perturbation Adversarial Vulnerability Multimodal Attack

August 20, 2024

August 17, 2024

August 16, 2024

Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
Zhongjian Zhang, Xiao Wang, Huichi Zhou, Yue Yu, Mengmei Zhang, Cheng Yang, Chuan Shi
Graph Neural Network Native Robustness Adversarial Robustness Adversarial Perturbation Robust Graph

Adversarial Perturbation

Papers

Classification-Denoising Networks

Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization

MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning

Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises

Adversarial Watermarking for Face Recognition

Cloud Adversarial Example Generation for Remote Sensing Image Classification

Efficient Visualization of Neural Networks with Generative Models and Adversarial Perturbations

Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach

Contextual Breach: Assessing the Robustness of Transformer-based QA Models

2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures

Analyzing Inference Privacy Risks Through Gradients in Machine Learning

Certified Causal Defense with Generalizable Robustness

Dual Adversarial Perturbators Generate rich Views for Recommendation

2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach

MsMemoryGAN: A Multi-scale Memory GAN for Palm-vein Adversarial Purification

Robust Image Classification: Defensive Strategies against FGSM and PGD Adversarial Attacks

Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model

Attack Anything: Blind DNNs via Universal Background Adversarial Attack

Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?