DeBias-Attack corrects surrogate-specific bias in adversarial gradients for VLP models by subtracting the projection from a reference branch optimized on weak-semantic images.
Improving adversarial transferability of vision-language pre-training models through collaborative multimodal interaction,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
JECA^2 is a new white-box attack method using Grad-CAM-guided perturbations and prompt embedding optimization to achieve judgment-explanation consistent adversarial attacks on forensic VLMs.
VLA-Hijack is a new adversarial patch attack on Vision-Language-Action models that suppresses real arm features and injects the patch as surrogate embodiment to achieve high cross-architecture transferability.
citing papers explorer
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Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction
DeBias-Attack corrects surrogate-specific bias in adversarial gradients for VLP models by subtracting the projection from a reference branch optimized on weak-semantic images.
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JECA^2: Judgment-Explanation Consistent Adversarial Attack against Forensic Vision-Language Models
JECA^2 is a new white-box attack method using Grad-CAM-guided perturbations and prompt embedding optimization to achieve judgment-explanation consistent adversarial attacks on forensic VLMs.
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VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking
VLA-Hijack is a new adversarial patch attack on Vision-Language-Action models that suppresses real arm features and injects the patch as surrogate embodiment to achieve high cross-architecture transferability.