3 papers
cs.CV2026
Hierarchical Refinement of Universal Multimodal Attacks on Vision-Language Models
Peng-Fei Zhang, Zi Huang
Existing adversarial attacks for VLP models are mostly sample-specific, resulting in substantial computational overhead when scaled to large datasets or new scenarios. To overcome…
cs.CV2025
MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models
Peng-Fei Zhang, Guangdong Bai, Zi Huang
Current adversarial attacks for evaluating the robustness of vision-language pre-trained (VLP) models in multi-modal tasks suffer from limited transferability, where attacks crafte…
cs.LG2025
GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation
Danny Wang, Ruihong Qiu, Guangdong Bai +1
Despite graph neural networks' (GNNs) great success in modelling graph-structured data, out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One…