37 citations · 71 across the 13 of their papers we have counts for
5 papers · 1 filter
Fast and Lightweight Backdoor Detection via Head Random Probing
Yinbo Yu, Xueyu Yin, Jing Fang +4
Deep neural networks (DNNs) remain critically vulnerable to backdoor attacks. Existing post-training detectors often require clean or surrogate data, gradients, or iterative trigge…
Lightweight and Fast Backdoor Model Detection
Yinbo Yu, Jing Fang, Xuewen Zhang +4
Deep neural networks (DNN), despite their remarkable performance, are highly vulnerable to backdoor attacks. Existing defenses mainly rely on activation anomaly analysis or trigger…
TAPFixer: Automatic Detection and Repair of Home Automation Vulnerabilities based on Negated-property Reasoning
Yinbo Yu, Yuanqi Xu, Kepu Huang +1
Trigger-Action Programming (TAP) is a popular end-user programming framework in the home automation (HA) system, which eases users to customize home automation and control devices…
Don't Watch Me: A Spatio-Temporal Trojan Attack on Deep-Reinforcement-Learning-Augment Autonomous Driving
Yinbo Yu, Jiajia Liu
Deep reinforcement learning (DRL) is one of the most popular algorithms to realize an autonomous driving (AD) system. The key success factor of DRL is that it embraces the percepti…
TAPInspector: Safety and Liveness Verification of Concurrent Trigger-Action IoT Systems
Yinbo Yu, Jiajia Liu
Trigger-action programming (TAP) is a popular end-user programming framework that can simplify the Internet of Things (IoT) automation with simple trigger-action rules. However, it…