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
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
Miguel Costa, Sandro Pinto
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack
Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne, Sanjay Chawla
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods
Roopkatha Dey, Aivy Debnath, Sayak Kumar Dutta, Kaustav Ghosh, Arijit Mitra, Arghya Roy Chowdhury, Jaydip Sen
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?
Anjun Hu, Jindong Gu, Francesco Pinto, Konstantinos Kamnitsas, Philip Torr
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory
Sensen Gao, Xiaojun Jia, Xuhong Ren, Ivor Tsang, Qing Guo