Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of
  Pre-trained Multimodal Representation via Text Updates
Authors: name: Jaewoo Ahn, name: Heeseung Yun, name: Dayoon Ko, name: Gunhee Kim

While pre-trained multimodal representations (e.g., CLIP) have shown
impressive capabilities, they exhibit significant compositional vulnerabilities
leading to counterintuitive judgments. We introduce Multimodal Adversarial
Compositionality (MAC), a benchmark that leverages large language models (LLMs)
to generate deceptive text samples to exploit these vulnerabilities across
different modalities and evaluates them through both sample-wise attack success
rate and group-wise entropy-based diversity. To improve zero-shot methods, we
propose a self-training approach that leverages rejection-sampling fine-tuning
with diversity-promoting filtering, which enhances both attack success rate and
sample diversity. Using smaller language models like Llama-3.1-8B, our approach
demonstrates superior performance in revealing compositional vulnerabilities
across various multimodal representations, including images, videos, and
audios.