3 citations · 4 across the 13 of their papers we have counts for
13 papers
TYPO: Instruction-Dense Visual Jailbreaks against Commercial Closed-Source Image-Generation Models
Meng Xie, Li Zeng, Hangtao Zhang +4
Recent commercial image-generation models can generate high-quality images with readable text (e.g., posters, infographics, and manuals), attracting considerable attention. Yet we…
GhostPrompt: Cross-Image Adversarial Prompt for Vision-Language Models
Li Zeng, Zeyu Ye, Meng Xie +4
Vision-Language Models (VLMs) are known to be vulnerable to adversarial attacks, where subtle perturbations to images or texts induce erroneous outputs. However, most text-based at…
LHGstore: An In-Memory Learned Graph Storage for Fast Updates and Analytics
Pengpeng Qiao, Zhiwei Zhang, Xinzhou Wang +3
Various real-world applications rely on in-memory dynamic graphs that must efficiently handle frequent updates while supporting low-latency analytics on evolving structures. Achiev…
HoGS: Homophily-Oriented Graph Synthesis for Local Differentially Private GNN Training
Wen Xu, Zhetao Li, Yong Xiao +3
Graph neural networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks by effectively modeling high-order interactions between nodes. H…
RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization
Yuxia Sun, Huihong Chen, Jingcai Guo +3
Attributing APT (Advanced Persistent Threat) malware to their respective groups is crucial for threat intelligence and cybersecurity. However, APT adversaries often conceal their i…
Quark: Implementing Convolutional Neural Networks Entirely on Programmable Data Plane
Mai Zhang, Lin Cui, Xiaoquan Zhang +4
The rapid development of programmable network devices and the widespread use of machine learning (ML) in networking have facilitated efficient research into intelligent data plane…
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
Shuo Liu, Di Yao, Lanting Fang +5
Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, fina…
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
Hangtao Zhang, Zeming Yao, Leo Yu Zhang +4
Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisonin…
Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC Networks
Shao-Di Wang, Hui-Ming Wang, Zhetao Li +1
For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection c…
Decentralized Multi-AGV Task Allocation based on Multi-Agent Reinforcement Learning with Information Potential Field Rewards
Mengyuan Li, Bin Guo, Jiangshan Zhang +5
Automated Guided Vehicles (AGVs) have been widely used for material handling in flexible shop floors. Each product requires various raw materials to complete the assembly in produc…
Mobility Inference on Long-Tailed Sparse Trajectory
Lei Shi
Analyzing the urban trajectory in cities has become an important topic in data mining. How can we model the human mobility consisting of stay and travel from the raw trajectory dat…
Secrecy and Covert Communications against UAV Surveillance via Multi-Hop Networks
Hui-Ming Wang, Yan Zhang, Xu Zhang +1
The deployment of unmanned aerial vehicle (UAV) for surveillance and monitoring gives rise to the confidential information leakage challenge in both civilian and military environme…
Greedy Sparse Signal Reconstruction Using Matching Pursuit Based on Hope-tree
Zhetao Li, Hongqing Zeng, Chengqing Li +1
The reconstruction of sparse signals requires the solution of an -norm minimization problem in Compressed Sensing. Previous research has focused on the investigation of a s…