6 papers
BadGraph: A Backdoor Attack Against Latent Diffusion Model for Text-Guided Graph Generation
Liang Ye, Shengqin Chen, Jiazhu Dai
The rapid progress of graph generation has raised new security concerns, particularly regarding backdoor vulnerabilities. Though prior work has explored backdoor attacks against di…
RefSR-Adv: Adversarial Attack on Reference-based Image Super-Resolution Models
Jiazhu Dai, Huihui Jiang
Single Image Super-Resolution (SISR) aims to recover high-resolution images from low-resolution inputs. Unlike SISR, Reference-based Super-Resolution (RefSR) leverages an additiona…
Effective backdoor attack on graph neural networks in link prediction tasks
Jiazhu Dai, Haoyu Sun
Graph Neural Networks (GNNs) are a class of deep learning models capable of processing graph-structured data, and they have demonstrated significant performance in a variety of rea…
Graph-Level Label-Only Membership Inference Attack against Graph Neural Networks
Jiazhu Dai, Yubing Lu
Graph neural networks (GNNs) are widely used for graph-structured data but are vulnerable to membership inference attacks (MIAs) in graph classification tasks, which determine if a…
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks
Jiazhu Dai, Haoyu Sun
Graph Convolutional Networks (GCNs) have shown excellent performance in graph-structured tasks such as node classification and graph classification. However, recent research has sh…
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only
Jiazhu Dai, Haoyu Sun
Graph Convolutional Networks (GCNs) have shown excellent performance in dealing with various graph structures such as node classification, graph classification and other tasks. How…