CLIP-Based Model Alignment Degradation on MSCOCO Retrieval Under Frequency-Domain Adversarial Noise

Abstract

Abstract: Benchmark accuracy is often implicitly assumed to reflect grounded visual understanding in vision-language models (VLMs), yet it remains unclear to what extent such scores truly reflect reliance on visual evidence. Motivated by a surprising observation that removing a substantial fraction of image tokens only degrades model performance very slightly on a widely used hallucination benchmark, we systematically investigate this mismatch in a set of open-source VLMs. Our analysis spans multiple levels of granularity, spanning global visual degradation, localized occlusion, question reformulation,

Research Question

To what extent does adversarial noise in the frequency domain degrade the alignment performance of CLIP-based models on the MSCOCO retrieval benchmark?

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

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