What is the robustness of SMoES to distribution shifts in multimodal inputs (e.g., adversarial image perturbat

Abstract

Abstract: Pre-trained vision-language (VL) models are highly vulnerable to adversarial attacks. However, existing defense methods primarily focus on image classification, overlooking two key aspects of VL tasks: multimodal attacks, where both image and text can be perturbed, and the one-to-many relationship of images and texts, where a single image can correspond to multiple textual descriptions and vice versa (1:N and N:1). This work is the first to explore defense strategies against multimodal attacks in VL tasks, whereas prior VL defense methods focus on vision robustness. We propose multimodal adver

Research Question

What is the robustness of SMoES to distribution shifts in multimodal inputs (e.g., adversarial image perturbations or text paraphrasing) compared to standard MoE routing, evaluated by accuracy drop on the RobustVQA or A-OKVQA benchmarks?

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