12 citations · 13 across the 8 of their papers we have counts for
9 papers
Under the Skin: Backdoor Infection in Multimodal Large Language Models
Jiawen Wang, Ruo Yang, Haoran Dai +2
Multimodal Large Language Models (MLLMs) have achieved impressive performance in generating text conditioned on inputs from different modalities. In this work, we introduce backdoo…
MoCo-EA: Exploiting Adversarial Mode Connectivity for Efficient Evolutionary Attacks
Hyo Seo Kim, Gang Luo, Can Chen +3
Evolutionary algorithms for adversarial attacks leverage population-based search to discover perturbations without gradient information, but suffer from inefficient crossover opera…
On Google's SynthID-Text LLM Watermarking System: Theoretical Analysis and Empirical Validation
Romina Omidi, Yun Dong, Binghui Wang
Google's SynthID-Text, the first ever production-ready generative watermark system for large language model, designs a novel Tournament-based method that achieves the state-of-the-…
When One Modality Rules Them All: Backdoor Modality Collapse in Multimodal Diffusion Models
Qitong Wang, Haoran Dai, Haotian Zhang +2
While diffusion models have revolutionized visual content generation, their rapid adoption has underscored the critical need to investigate vulnerabilities, e.g., to backdoor attac…
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
Jiate Li, Meng Pang, Yun Dong +1
Graph neural networks (GNNs) are becoming the de facto method to learn on the graph data and have achieved the state-of-the-art on node and graph classification tasks. However, rec…
Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks
Jiate Li, Meng Pang, Yun Dong +2
Explaining Graph Neural Network (XGNN) has gained growing attention to facilitate the trust of using GNNs, which is the mainstream method to learn graph data. Despite their growing…
AGNNCert: Defending Graph Neural Networks against Arbitrary Perturbations with Deterministic Certification
Jiate Li, Binghui Wang
Graph neural networks (GNNs) achieve the state-of-the-art on graph-relevant tasks such as node and graph classification. However, recent works show GNNs are vulnerable to adversari…
Graph Neural Network Explanations are Fragile
Jiate Li, Meng Pang, Yun Dong +2
Explainable Graph Neural Network (GNN) has emerged recently to foster the trust of using GNNs. Existing GNN explainers are developed from various perspectives to enhance the explan…
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
Wei Zou, Runpeng Geng, Binghui Wang +1
Large language models (LLMs) have achieved remarkable success due to their exceptional generative capabilities. Despite their success, they also have inherent limitations such as a…