37 citations · 71 across the 13 of their papers we have counts for
13 papers
Split-Gate Pooled-Evidence Stochastic-Rollout Scheduling for Timely Progressive Edge Inference
Sai Xu, Yinbo Yu, Yanan Du +2
This paper investigates causal radio scheduling for progressive edge inference, with the goal of maximizing timely inference throughput under job-specific deadlines. Specifically,…
A Benchmark Dataset for MLLM-Generated Image Detection: GPT Image2 & Nano Banana2
Zirui Zhang, Yinbo Yu, Donghai Guan +3
The realism of images generated by multimodal large language models (MLLMs), such as GPT Image2 and Nano Banana2, has improved rapidly in recent years. Compared with early generati…
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…
BehaviorGuard: Online Backdoor Defense for Deep Reinforcement Learning
Yinbo Yu, Xueyu Yin, Jiadai Wang +4
Backdoor attacks pose a serious threat to deep reinforcement learning (DRL). Current defenses typically rely on reward anomalies to reverse-engineer triggers and model finetuning t…
BLAST: A Stealthy Backdoor Leverage Attack against Cooperative Multi-Agent Deep Reinforcement Learning based Systems
Jing Fang, Saihao Yan, Xueyu Yin +3
Recent studies have shown that cooperative multi-agent deep reinforcement learning (c-MADRL) is under the threat of backdoor attacks. Once a backdoor trigger is observed, it will p…
A Spatiotemporal Stealthy Backdoor Attack against Cooperative Multi-Agent Deep Reinforcement Learning
Yinbo Yu, Saihao Yan, Jiajia Liu
Recent studies have shown that cooperative multi-agent deep reinforcement learning (c-MADRL) is under the threat of backdoor attacks. Once a backdoor trigger is observed, it will p…
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…
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information
Jing Fang, Yinbo Yu, Zhongyuan Wang +2
Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Spatial-domain information has been widely exploited to impleme…
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…
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning
Yinbo Yu, Jiajia Liu, Shouqing Li +2
Deep reinforcement learning (DRL) has made significant achievements in many real-world applications. But these real-world applications typically can only provide partial observatio…
Generative Adversarial Networks for Image Super-Resolution: A Survey
Ziang Wu, Xuanyu Zhang, Yinbo Yu +3
Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on l…
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…