1 citations · 2 across the 7 of their papers we have counts for
7 papers
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-…
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…
Learning Robust and Privacy-Preserving Representations via Information Theory
Binghui Zhang, Sayedeh Leila Noorbakhsh, Yun Dong +2
Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to miti…
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…
Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning Against Attribute Inference Attacks
Caridad Arroyo Arevalo, Sayedeh Leila Noorbakhsh, Yun Dong +2
Federated learning (FL) has been widely studied recently due to its property to collaboratively train data from different devices without sharing the raw data. Nevertheless, recent…
Turning Strengths into Weaknesses: A Certified Robustness Inspired Attack Framework against Graph Neural Networks
Binghui Wang, Meng Pang, Yun Dong
Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph learning tasks. However, recent studies show that GNNs are vulnerable to both test-time evasio…