4 papers
Fine-grained Distributed Backdoor Attacks in Federated Learning
Jian Wang, Hong Shen, Wei Ke +1
Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centralized attacks, distributed bac…
Structure-Aware Distributed Backdoor Attacks in Federated Learning
Wang Jian, Shen Hong, Ke Wei +1
While federated learning protects data privacy, it also makes the model update process vulnerable to long-term stealthy perturbations. Existing studies on backdoor attacks in feder…
Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learning
Jian Wang, Hong Shen, Chan-Tong Lam
Traditional distributed backdoor attacks (DBA) in federated learning improve stealthiness by decomposing global triggers into sub-triggers, which however requires more poisoned dat…
Efficient Jailbreaking of Large Models by Freeze Training: Lower Layers Exhibit Greater Sensitivity to Harmful Content
Hongyuan Shen, Min Zheng, Jincheng Wang +1
With the widespread application of Large Language Models across various domains, their security issues have increasingly garnered significant attention from both academic and indus…