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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★ 1 cited
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.CV2024★ 1 cited
Universal Adversarial Perturbations for Vision-Language Pre-trained Models
Peng-Fei Zhang, Zi Huang, Guangdong Bai
Vision-language pre-trained (VLP) models have been the foundation of numerous vision-language tasks. Given their prevalence, it becomes imperative to assess their adversarial robus…