3 citations · 7 across the 41 of their papers we have counts for
8 papers · 1 filter
WFCAT: Augmenting Website Fingerprinting with Channel-wise Attention on Timing Features
Jiajun Gong, Wei Cai, Siyuan Liang +3
Website Fingerprinting (WF) aims to deanonymize users on the Tor network by analyzing encrypted network traffic. Recent deep-learning-based attacks show high accuracy on undefended…
TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning
Siyuan Liang, Jiajun Gong, Tianmeng Fang +5
Website fingerprinting (WF) attacks, which covertly monitor user communications to identify the web pages they visit, pose a serious threat to user privacy. Existing WF defenses at…
CopyrightShield: Enhancing Diffusion Model Security against Copyright Infringement Attacks
Zhixiang Guo, Siyuan Liang, Aishan Liu +1
Diffusion models have attracted significant attention due to its exceptional data generation capabilities in fields such as image synthesis. However, recent studies have shown that…
BDefects4NN: A Backdoor Defect Database for Controlled Localization Studies in Neural Networks
Yisong Xiao, Aishan Liu, Xinwei Zhang +6
Pre-trained large deep learning models are now serving as the dominant component for downstream middleware users and have revolutionized the learning paradigm, replacing the tradit…
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving
Tianyuan Zhang, Lu Wang, Xinwei Zhang +7
Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain highly vulnerable to adversarial…
NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models
Zheng Yi Ho, Siyuan Liang, Sen Zhang +2
Hallucinations in Large Language Models (LLMs) remain a major obstacle, particularly in high-stakes applications where factual accuracy is critical. While representation editing an…
SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models
Zonghao Ying, Aishan Liu, Siyuan Liang +5
Multimodal Large Language Models (MLLMs) are showing strong safety concerns (e.g., generating harmful outputs for users), which motivates the development of safety evaluation bench…
Environmental Matching Attack Against Unmanned Aerial Vehicles Object Detection
Dehong Kong, Siyuan Liang, Wenqi Ren
Object detection techniques for Unmanned Aerial Vehicles (UAVs) rely on Deep Neural Networks (DNNs), which are vulnerable to adversarial attacks. Nonetheless, adversarial patches g…