Sample-agnostic Adversarial Perturbation for Vision-Language Pre-training Models

TL;DR

This paper introduces a novel universal, sample-agnostic adversarial perturbation method for vision-language pre-training models, demonstrating its effectiveness and transferability across multiple models and datasets.

Contribution

It is the first work to develop a universal adversarial perturbation for multimodal models by leveraging decision boundaries in the input space.

Findings

Universal perturbation successfully impairs VLP model performance

Method transfers across different models and datasets

Supports creation of global perturbations and adversarial patches

Abstract

Recent studies on AI security have highlighted the vulnerability of Vision-Language Pre-training (VLP) models to subtle yet intentionally designed perturbations in images and texts. Investigating multimodal systems' robustness via adversarial attacks is crucial in this field. Most multimodal attacks are sample-specific, generating a unique perturbation for each sample to construct adversarial samples. To the best of our knowledge, it is the first work through multimodal decision boundaries to explore the creation of a universal, sample-agnostic perturbation that applies to any image. Initially, we explore strategies to move sample points beyond the decision boundaries of linear classifiers, refining the algorithm to ensure successful attacks under the top $k$ accuracy metric. Based on this foundation, in visual-language tasks, we treat visual and textual modalities as reciprocal sample points and decision hyperplanes, guiding image embeddings to traverse text-constructed decision boundaries, and vice versa. This iterative process consistently refines a universal perturbation, ultimately identifying a singular direction within the input space which is exploitable to impair the retrieval performance of VLP models. The proposed algorithms support the creation of global perturbations or adversarial patches. Comprehensive experiments validate the effectiveness of our method, showcasing its data, task, and model transferability across various VLP models and datasets. Code: https://github.com/LibertazZ/MUAP