3 papers
cs.CV2026
Seeing No Evil: Blinding Large Vision-Language Models to Safety Instructions via Adversarial Attention Hijacking
Jingru Li, Wei Ren, Tianqing Zhu
Large Vision-Language Models (LVLMs) rely on attention-based retrieval of safety instructions to maintain alignment during generation. Existing attacks typically optimize image per…
cs.LG2025
Graph Unlearning: Efficient Node Removal in Graph Neural Networks
Faqian Guan, Tianqing Zhu, Zhoutian Wang +2
With increasing concerns about privacy attacks and potential sensitive information leakage, researchers have actively explored methods to efficiently remove sensitive training data…
cs.CR2024
Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networks
Faqian Guan, Tianqing Zhu, Wenhan Chang +2
Graph Neural Networks (GNNs), specifically designed to process the graph data, have achieved remarkable success in various applications. Link stealing attacks on graph data pose a…