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
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis
Jiancong Xiao, Zeyu Qin, Yanbo Fan, Baoyuan Wu, Jue Wang, Zhi-Quan Luo
Understanding Adversarial Robustness Against On-manifold Adversarial Examples
Jiancong Xiao, Liusha Yang, Yanbo Fan, Jue Wang, Zhi-Quan Luo
Learning Robust Kernel Ensembles with Kernel Average Pooling
Pouya Bashivan, Adam Ibrahim, Amirozhan Dehghani, Yifei Ren
Your Out-of-Distribution Detection Method is Not Robust!
Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei, Reihaneh Zohrabi, Mohammad Taghi Manzuri, Mohammad Hossein Rohban
A Survey on Physical Adversarial Attack in Computer Vision
Donghua Wang, Wen Yao, Tingsong Jiang, Guijian Tang, Xiaoqian Chen
Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models
Youness Moukafih, Mounir Ghogho, Kamel Smaili
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Aishan Liu, Shiyu Tang, Siyuan Liang, Ruihao Gong, Boxi Wu, Xianglong Liu, Dacheng Tao