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Textual Response-Aware Guidance for Zero-Shot Adversarial Robustness of Vision-Language Models
Vision-language models (VLMs) exhibit strong zero-shot generalization but remain vulnerable to adversarial attacks. Existing adversarial fine-tuning methods often use pre-trained model guidance to preserve image--text alignment while improving robustness. Our analysis of visual representation drift
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