Backdoor Attack
Backdoor attacks exploit vulnerabilities in machine learning models by embedding hidden triggers during training, causing the model to produce malicious outputs when the trigger is present. Current research focuses on developing and mitigating these attacks across various model architectures, including deep neural networks, vision transformers, graph neural networks, large language models, and spiking neural networks, with a particular emphasis on understanding attack mechanisms and developing robust defenses in federated learning and generative models. The significance of this research lies in ensuring the trustworthiness and security of increasingly prevalent machine learning systems across diverse applications, ranging from object detection and medical imaging to natural language processing and autonomous systems.
Papers
Backdoor Attack on Vertical Federated Graph Neural Network Learning
Jirui Yang, Peng Chen, Zhihui Lu, Ruijun Deng, Qiang Duan, Jianping Zeng
AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignment
Pankayaraj Pathmanathan, Udari Madhushani Sehwag, Michael-Andrei Panaitescu-Liess, Furong Huang
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning
Hassan Ali, Surya Nepal, Salil S. Kanhere, Sanjay Jha
"No Matter What You Do": Purifying GNN Models via Backdoor Unlearning
Jiale Zhang, Chengcheng Zhu, Bosen Rao, Hao Sui, Xiaobing Sun, Bing Chen, Chunyi Zhou, Shouling Ji
Backdooring Vision-Language Models with Out-Of-Distribution Data
Weimin Lyu, Jiachen Yao, Saumya Gupta, Lu Pang, Tao Sun, Lingjie Yi, Lijie Hu, Haibin Ling, Chao Chen