Impact of Feature-Level Adversarial Perturbations on CLIP Robustness in Retrieval Tasks

Abstract

Abstract: Contrastive Language-Image Pre-training (CLIP) models have shown significant potential, particularly in zero-shot classification across diverse distribution shifts. Building on existing evaluations of overall classification robustness, this work aims to provide a more comprehensive assessment of CLIP by introducing several new perspectives. First, we investigate their robustness to variations in specific visual factors. Second, we assess two critical safety objectives–confidence uncertainty and out-of-distribution detection–beyond mere classification accuracy. Third, we evaluate the finesse

Research Question

What is the impact of feature-level adversarial perturbations during multimodal pretraining on the robustness of CLIP models against common corruptions in retrieval tasks?

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