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
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models
Pete Janowczyk, Linda Laurier, Ave Giulietta, Arlo Octavia, Meade Cleti
Attention Masks Help Adversarial Attacks to Bypass Safety Detectors
Yunfan Shi
Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging
Rui Luo, Jie Bao, Zhixin Zhou, Chuangyin Dang
Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras
Roberto Riaño, Gorka Abad, Stjepan Picek, Aitor Urbieta
Region-Guided Attack on the Segment Anything Model (SAM)
Xiaoliang Liu, Furao Shen, Jian Zhao
Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning
Jinyin Chen, Wenbo Mu, Luxin Zhang, Guohan Huang, Haibin Zheng, Yao Cheng
Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignment
Jason Vega, Junsheng Huang, Gaokai Zhang, Hangoo Kang, Minjia Zhang, Gagandeep Singh
Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack
Xiaojun Jia, Sensen Gao, Qing Guo, Ke Ma, Yihao Huang, Simeng Qin, Yang Liu, Ivor Tsang Fellow, Xiaochun Cao
Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs
Xiaoqing Chen, Ziwei Wang, Dongrui Wu
LiDAttack: Robust Black-box Attack on LiDAR-based Object Detection
Jinyin Chen, Danxin Liao, Sheng Xiang, Haibin Zheng
Protecting Feed-Forward Networks from Adversarial Attacks Using Predictive Coding
Ehsan Ganjidoost, Jeff Orchard
Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey
Chiyu Zhang, Xiaogang Xu, Jiafei Wu, Zhe Liu, Lu Zhou
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
Weichao Zhou, Wenchao Li
Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System
Julian Collado, Kevin Stangl
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
Tejaswini Medi, Steffen Jung, Margret Keuper
Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector
Youcheng Huang, Fengbin Zhu, Jingkun Tang, Pan Zhou, Wenqiang Lei, Jiancheng Lv, Tat-Seng Chua