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
Unscrambling the Rectification of Adversarial Attacks Transferability across Computer Networks
Ehsan Nowroozi, Samaneh Ghelichkhani, Imran Haider, Ali Dehghantanha
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Jindong Gu, Xiaojun Jia, Pau de Jorge, Wenqain Yu, Xinwei Liu, Avery Ma, Yuan Xun, Anjun Hu, Ashkan Khakzar, Zhijiang Li, Xiaochun Cao, Philip Torr
Instability of computer vision models is a necessary result of the task itself
Oliver Turnbull, George Cevora
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks
Aradhana Sinha, Ananth Balashankar, Ahmad Beirami, Thi Avrahami, Jilin Chen, Alex Beutel
RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery
Fan Xing, Xiaoyi Zhou, Xuefeng Fan, Zhuo Tian, Yan Zhao