Adversarial Attack
Adversarial attacks aim to deceive machine learning models by subtly altering input data, causing misclassifications or other erroneous outputs. Current research focuses on developing more robust models and detection methods, exploring various attack strategies across different model architectures (including vision transformers, recurrent neural networks, and graph neural networks) and data types (images, text, signals, and tabular data). Understanding and mitigating these attacks is crucial for ensuring the reliability and security of AI systems in diverse applications, from autonomous vehicles to medical diagnosis and cybersecurity.
Papers
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
Tengyuan Liang
Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance
Ngoc N. Tran, Anh Tuan Bui, Dinh Phung, Trung Le
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi, Damith C. Ranasinghe
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee, Thomas A. Henzinger, Daniela Rus
Understanding and Enhancing Robustness of Concept-based Models
Sanchit Sinha, Mengdi Huai, Jianhui Sun, Aidong Zhang
Attack on Unfair ToS Clause Detection: A Case Study using Universal Adversarial Triggers
Shanshan Xu, Irina Broda, Rashid Haddad, Marco Negrini, Matthias Grabmair
Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces
Xiaoqing Chen, Dongrui Wu
Imperceptible Adversarial Attack via Invertible Neural Networks
Zihan Chen, Ziyue Wang, Junjie Huang, Wentao Zhao, Xiao Liu, Dejian Guan