Adversarial Example
Adversarial examples are subtly altered inputs designed to fool machine learning models, primarily deep neural networks (DNNs), into making incorrect predictions. Current research focuses on improving model robustness against these attacks, exploring techniques like ensemble methods, multi-objective representation learning, and adversarial training, often applied to architectures such as ResNets and Vision Transformers. Understanding and mitigating the threat of adversarial examples is crucial for ensuring the reliability and security of AI systems across diverse applications, from image classification and natural language processing to malware detection and autonomous driving. The development of robust defenses and effective attack detection methods remains a significant area of ongoing investigation.
Papers
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
Wenkai Tan, Justus Renkhoff, Alvaro Velasquez, Ziyu Wang, Lusi Li, Jian Wang, Shuteng Niu, Fan Yang, Yongxin Liu, Houbing Song
Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples
Ali Montazeralghaem, James Allan
BeamAttack: Generating High-quality Textual Adversarial Examples through Beam Search and Mixed Semantic Spaces
Hai Zhu, Qingyang Zhao, Yuren Wu
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation
Geunhyeok Yu, Minwoo Jeon, Hyoseok Hwang
Immune Defense: A Novel Adversarial Defense Mechanism for Preventing the Generation of Adversarial Examples
Jinwei Wang, Hao Wu, Haihua Wang, Jiawei Zhang, Xiangyang Luo, Bin Ma
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
Justus Renkhoff, Wenkai Tan, Alvaro Velasquez, illiam Yichen Wang, Yongxin Liu, Jian Wang, Shuteng Niu, Lejla Begic Fazlic, Guido Dartmann, Houbing Song
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression
Junho Kim, Byung-Kwan Lee, Yong Man Ro
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
Odelia Melamed, Gilad Yehudai, Gal Vardi
To Make Yourself Invisible with Adversarial Semantic Contours
Yichi Zhang, Zijian Zhu, Hang Su, Jun Zhu, Shibao Zheng, Yuan He, Hui Xue
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process
Mingze Ni, Zhensu Sun, Wei Liu
Less is More: Data Pruning for Faster Adversarial Training
Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan Goldhahn
A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots
Boyang Zhang, Xinlei He, Yun Shen, Tianhao Wang, Yang Zhang
Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective
Zhengbao He, Tao Li, Sizhe Chen, Xiaolin Huang