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
Layer Pruning with Consensus: A Triple-Win Solution
Leandro Giusti Mugnaini, Carolina Tavares Duarte, Anna H. Reali Costa, Artur Jordao
Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders
Alexander Stevens, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt
Global Challenge for Safe and Secure LLMs Track 1
Xiaojun Jia, Yihao Huang, Yang Liu, Peng Yan Tan, Weng Kuan Yau, Mun-Thye Mak, Xin Ming Sim, Wee Siong Ng, See Kiong Ng, Hanqing Liu, Lifeng Zhou, Huanqian Yan, Xiaobing Sun, Wei Liu, Long Wang, Yiming Qian, Yong Liu, Junxiao Yang, Zhexin Zhang, Leqi Lei, Renmiao Chen, Yida Lu, Shiyao Cui, Zizhou Wang, Shaohua Li, Yan Wang, Rick Siow Mong Goh, Liangli Zhen, Yingjie Zhang, Zhe Zhao
Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation
Ke Zhao (1), Huayang Huang (1), Miao Li (1), Yu Wu (1) ((1) Wuhan University)
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
Junae Kim, Amardeep Kaur
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods
Egor Kovalev, Georgii Bychkov, Khaled Abud, Aleksandr Gushchin, Anna Chistyakova, Sergey Lavrushkin, Dmitriy Vatolin, Anastasia Antsiferova
Few-shot Model Extraction Attacks against Sequential Recommender Systems
Hui Zhang, Fu Liu
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
Taowen Wang, Dongfang Liu, James Chenhao Liang, Wenhao Yang, Qifan Wang, Cheng Han, Jiebo Luo, Ruixiang Tang
Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations
Jianfeng Chi, Ujjwal Karn, Hongyuan Zhan, Eric Smith, Javier Rando, Yiming Zhang, Kate Plawiak, Zacharie Delpierre Coudert, Kartikeya Upasani, Mahesh Pasupuleti
Prompt-Guided Environmentally Consistent Adversarial Patch
Chaoqun Li, Huanqian Yan, Lifeng Zhou, Tairan Chen, Zhuodong Liu, Hang Su
Adversarial Attacks Using Differentiable Rendering: A Survey
Matthew Hull, Chao Zhang, Zsolt Kira, Duen Horng Chau
Enhancing generalization in high energy physics using white-box adversarial attacks
Franck Rothen, Samuel Klein, Matthew Leigh, Tobias Golling
BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation
Zheng Zhou, Wenquan Feng, Shuchang Lyu, Guangliang Cheng, Xiaowei Huang, Qi Zhao
Deceiving Question-Answering Models: A Hybrid Word-Level Adversarial Approach
Jiyao Li, Mingze Ni, Yongshun Gong, Wei Liu
Can adversarial attacks by large language models be attributed?
Manuel Cebrian, Jan Arne Telle
Chain Association-based Attacking and Shielding Natural Language Processing Systems
Jiacheng Huang, Long Chen