8 citations · 11 across the 17 of their papers we have counts for
17 papers
DSPrompt: Dynamic Soft Prompt Defense Against M-RAG Corruption
Chang Liu, Yuni Lai, Mingyue Cui +5
Multimodal Retrieval Augmented Generation (M-RAG) is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce embeddings that align with benign en…
Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning
Xiao Yang, Yuni Lai, Gaolei Li +4
Semantic Communication (SC) backdoor attacks aim to utilize triggers to manipulate the system into producing predetermined outputs via backdoored shared knowledge. Current SC backd…
Stealthy Dual-Trigger Backdoors: Attacking Prompt Tuning in LM-Empowered Graph Foundation Models
Xiaoyu Xue, Yuni Lai, Chenxi Huang +4
The emergence of graph foundation models (GFMs), particularly those incorporating language models (LMs), has revolutionized graph learning and demonstrated remarkable performance o…
Provably Robust Adaptation for Language-Empowered Foundation Models
Yuni Lai, Xiaoyu Xue, Linghui Shen +5
Language-empowered foundation models (LeFMs), such as CLIP and GraphCLIP, have transformed multimodal learning by aligning visual (or graph) features with textual representations,…
Multi-Grained Temporal-Spatial Graph Learning for Stable Traffic Flow Forecasting
Zhenan Lin, Yuni Lai, Wai Lun Lo +5
Time-evolving traffic flow forecasting are playing a vital role in intelligent transportation systems and smart cities. However, the dynamic traffic flow forecasting is a highly no…
AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing
Yuni Lai, Yulin Zhu, Yixuan Sun +6
Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness. Certified robustness via randomized smoothing offers provable guarant…
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models
Xiao Yang, Kai Zhou, Yuni Lai +1
With the trend of large graph learning models, business owners tend to employ a model provided by a third party to deliver business services to users. However, these models might b…
Collective Certified Robustness against Graph Injection Attacks
Yuni Lai, Bailin Pan, Kaihuang Chen +2
We investigate certified robustness for GNNs under graph injection attacks. Existing research only provides sample-wise certificates by verifying each node independently, leading t…
Adversarial Robustness of Link Sign Prediction in Signed Graphs
Jialong Zhou, Xing Ai, Yuni Lai +7
Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as th…
Towards Robust Graph Structural Learning Beyond Homophily via Preserving Neighbor Similarity
Yulin Zhu, Yuni Lai, Xing Ai +7
Despite the tremendous success of graph-based learning systems in handling structural data, it has been widely investigated that they are fragile to adversarial attacks on homophil…
Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,
Yuwei Han, Yuni Lai, Yulin Zhu +1
Graph Neural Networks (GNNs) have become widely used in the field of graph mining. However, these networks are vulnerable to structural perturbations. While many research efforts h…
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks
Yuni Lai, Yulin Zhu, Bailin Pan +1
Deep Graph Learning (DGL) has emerged as a crucial technique across various domains. However, recent studies have exposed vulnerabilities in DGL models, such as susceptibility to e…
Black-Box Attacks against Signed Graph Analysis via Balance Poisoning
Jialong Zhou, Yuni Lai, Jian Ren +1
Signed graphs are well-suited for modeling social networks as they capture both positive and negative relationships. Signed graph neural networks (SGNNs) are commonly employed to p…
Coupled-Space Attacks against Random-Walk-based Anomaly Detection
Yuni Lai, Marcin Waniek, Liying Li +5
Random Walks-based Anomaly Detection (RWAD) is commonly used to identify anomalous patterns in various applications. An intriguing characteristic of RWAD is that the input graph ca…
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection
Yuni Lai, Yulin Zhu, Wenqi Fan +2
The robustness of recommender systems under node injection attacks has garnered significant attention. Recently, GraphRfi, a GNN-based recommender system, was proposed and shown to…
From Bi-Level to One-Level: A Framework for Structural Attacks to Graph Anomaly Detection
Yulin Zhu, Yuni Lai, Kaifa Zhao +5
The success of graph neural networks stimulates the prosperity of graph mining and the corresponding downstream tasks including graph anomaly detection (GAD). However, it has been…
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
Yulin Zhu, Yuni Lai, Kaifa Zhao +4
Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques. These GAD to…