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
Time Traveling to Defend Against Adversarial Example Attacks in Image Classification
Anthony Etim, Jakub Szefer
Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation
Tomas Bueno Momcilovic, Beat Buesser, Giulio Zizzo, Mark Purcell, Tomas Bueno Momcilovic
RAB$^2$-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients
Nuria Rodríguez-Barroso, M. Victoria Luzón, Francisco Herrera
A Survey on Physical Adversarial Attacks against Face Recognition Systems
Mingsi Wang, Jiachen Zhou, Tianlin Li, Guozhu Meng, Kai Chen
Average Certified Radius is a Poor Metric for Randomized Smoothing
Chenhao Sun, Yuhao Mao, Mark Niklas Müller, Martin Vechev
Understanding Model Ensemble in Transferable Adversarial Attack
Wei Yao, Zeliang Zhang, Huayi Tang, Yong Liu
PII-Scope: A Benchmark for Training Data PII Leakage Assessment in LLMs
Krishna Kanth Nakka, Ahmed Frikha, Ricardo Mendes, Xue Jiang, Xuebing Zhou
Break the Visual Perception: Adversarial Attacks Targeting Encoded Visual Tokens of Large Vision-Language Models
Yubo Wang, Chaohu Liu, Yanqiu Qu, Haoyu Cao, Deqiang Jiang, Linli Xu
Patch is Enough: Naturalistic Adversarial Patch against Vision-Language Pre-training Models
Dehong Kong, Siyuan Liang, Xiaopeng Zhu, Yuansheng Zhong, Wenqi Ren
AnyAttack: Towards Large-scale Self-supervised Generation of Targeted Adversarial Examples for Vision-Language Models
Jiaming Zhang, Junhong Ye, Xingjun Ma, Yige Li, Yunfan Yang, Jitao Sang, Dit-Yan Yeung
Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs
Tomas Bueno Momcilovic, Dian Balta, Beat Buesser, Giulio Zizzo, Mark Purcell
Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs
Tomas Bueno Momcilovic, Dian Balta, Beat Buesser, Giulio Zizzo, Mark Purcell
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization
Tung M. Luu, Thanh Nguyen, Tee Joshua Tian Jin, Sungwoon Kim, Chang D. Yoo