A Two-Stage Globally-Diverse Adversarial Attack for Vision-Language Pre-training Models

Wutao Chen; Huaqin Zou; Chen Wan; Lifeng Huang

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arXiv · 2601.12304

A Two-Stage Globally-Diverse Adversarial Attack for Vision-Language Pre-training Models

Wutao Chen·Huaqin Zou·Chen Wan·Lifeng Huang

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Abstract

Vision-language pre-training (VLP) models are vulnerable to adversarial examples, particularly in black-box scenarios. Existing multimodal attacks often suffer from limited perturbation diversity and unstable multi-stage pipelines. To address these challenges, we propose 2S-GDA, a two-stage globally-diverse attack framework. The proposed method first introduces textual perturbations through a globally-diverse strategy by combining candidate text expansion with globally-aware replacement. To enhance visual diversity, image-level perturbations are generated using multi-scale resizing and block-shuffle rotation. Extensive experiments on VLP models demonstrate that 2S-GDA consistently improves attack success rates over state-of-the-art methods, with gains of up to 11.17\% in black-box settings. Our framework is modular and can be easily combined with existing methods to further enhance adversarial transferability.

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Wutao Chen, Huaqin Zou, Chen Wan, Lifeng Huang. 2026-01-18. A Two-Stage Globally-Diverse Adversarial Attack for Vision-Language Pre-training Models. https://arxiv.org/abs/2601.12304

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