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
Model-tuning Via Prompts Makes NLP Models Adversarially Robust
Mrigank Raman, Pratyush Maini, J. Zico Kolter, Zachary C. Lipton, Danish Pruthi
Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems
Islam Debicha, Benjamin Cochez, Tayeb Kenaza, Thibault Debatty, Jean-Michel Dricot, Wim Mees
Do we need entire training data for adversarial training?
Vipul Gupta, Apurva Narayan
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems
Aminul Huq, Weiyi Zhang, Xiaolin Hu
Boosting Adversarial Attacks by Leveraging Decision Boundary Information
Boheng Zeng, LianLi Gao, QiLong Zhang, ChaoQun Li, JingKuan Song, ShuaiQi Jing
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
Learning the Legibility of Visual Text Perturbations
Dev Seth, Rickard Stureborg, Danish Pruthi, Bhuwan Dhingra
Identification of Systematic Errors of Image Classifiers on Rare Subgroups
Jan Hendrik Metzen, Robin Hutmacher, N. Grace Hua, Valentyn Boreiko, Dan Zhang
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