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

January 26, 2024

Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement
Nuoyan Zhou, Dawei Zhou, Decheng Liu, Xinbo Gao, Nannan Wang
Adversarial Training Adversarial Robustness Adversarial Perturbation Adversarial Sample Feature Disentanglement Adversarial Fine Tuning Bridging Gap

January 24, 2024

Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint
Zhongjie Shi, Fanghui Liu, Yuan Cao, Johan A. K. Suykens
Neural Network Deep Neural Network Adversarial Perturbation Robust Generalization Robust Overfitting

January 23, 2024

Fast Adversarial Training against Textual Adversarial Attacks
Yichen Yang, Xin Liu, Kun He
Adversarial Training Adversarial Robustness Adversarial Perturbation Textual Adversarial Attack Fast Adversarial Training

January 22, 2024

January 21, 2024

January 17, 2024

January 6, 2024

Transferable Learned Image Compression-Resistant Adversarial Perturbations
Yang Sui, Zhuohang Li, Ding Ding, Xiang Pan, Xiaozhong Xu, Shan Liu, Zhenzhong Chen
Adversarial Attack Adversarial Perturbation Image Compression Transferable Visual

January 4, 2024

Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging
Poojitha Thota, Jai Prakash Veerla, Partha Sai Guttikonda, Mohammad S. Nasr, Shirin Nilizadeh, Jacob M. Luber
Adversarial Attack Adversarial Example Adversarial Perturbation Textual Demonstration Robust Defense Adversarial Manipulation Multimodal Vision Language Model Pathology Imaging

December 21, 2023

ARBiBench: Benchmarking Adversarial Robustness of Binarized Neural Networks
Peng Zhao, Jiehua Zhang, Bowen Peng, Longguang Wang, YingMei Wei, Yu Liu, Li Liu
Neural Network Adversarial Robustness Supervised ImageNet Adversarial Perturbation

December 20, 2023

Doubly Perturbed Task Free Continual Learning
Byung Hyun Lee, Min-hwan Oh, Se Young Chun
Adversarial Perturbation Catastrophic Forgetting Online Continual Learning Free Continual Learning FL Framework

December 19, 2023

Rethinking Randomized Smoothing from the Perspective of Scalability
Anupriya Kumari, Devansh Bhardwaj, Sukrit Jindal
Timely Survey Adversarial Attack Native Robustness Adversarial Perturbation Appropriate Trust Randomized Smoothing Certified Defense

December 14, 2023

Scalable Ensemble-based Detection Method against Adversarial Attacks for speaker verification
Haibin Wu, Heng-Cheng Kuo, Yu Tsao, Hung-yi Lee
Adversarial Attack Adversarial Perturbation Speaker Verification Potential Scalability Adversarial Noise Purification Model

December 13, 2023

Causality Analysis for Evaluating the Security of Large Language Models
Wei Zhao, Zhe Li, Jun Sun
Adversarial Perturbation Security Related Causality Analysis Causal Strength

December 12, 2023

QuadAttack: A Quadratic Programming Approach to Ordered Top-K Attacks
Thomas Paniagua, Ryan Grainger, Tianfu Wu
Adversarial Perturbation Adversarial Vulnerability Gray Box Quadratic Programming

December 11, 2023

Towards Transferable Adversarial Attacks with Centralized Perturbation
Shangbo Wu, Yu-an Tan, Yajie Wang, Ruinan Ma, Wencong Ma, Yuanzhang Li
Adversarial Perturbation Transferable Attack Transferable Adversarial Attack Adversarial Transferability

December 8, 2023