Performance Degradation of LSA Framework in Vision-Language Benchmarks Under Black-Box Attacks

Abstract

Abstract: Despite the substantial advancements in Vision-Language Pre-training (VLP) models, their susceptibility to adversarial attacks poses a significant challenge. Existing work rarely studies the transferability of attacks on VLP models, resulting in a substantial performance gap from white-box attacks. We observe that prior work overlooks the interaction mechanisms between modalities, which plays a crucial role in understanding the intricacies of VLP models. In response, we propose a novel attack, called Collaborative Multimodal Interaction Attack (CMI-Attack), leveraging modality interaction thro

Research Question

How does the LSA framework's performance degradation ratio compare to other adversarial training methods (e.g., AT, TRADES) on standard vision-language benchmarks like VQA or Flickr30K under black-box attacks?

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