On the Feasibility of Poisoning Text-to-Image AI Models via Adversarial Mislabeling

TL;DR

This paper investigates how adversarial mislabeling attacks on vision-language models can poison training data for text-to-image AI models, demonstrating high attack success rates and highlighting vulnerabilities in current defenses.

Contribution

It reveals the vulnerability of vision-language models to adversarial mislabeling attacks and demonstrates their potential to poison training data for text-to-image models.

Findings

VLMs are highly vulnerable to adversarial perturbations.

Adversarial mislabeling can inject effective poison samples into training.

High attack success rate (>73%) in black-box scenarios.

Abstract

Today's text-to-image generative models are trained on millions of images sourced from the Internet, each paired with a detailed caption produced by Vision-Language Models (VLMs). This part of the training pipeline is critical for supplying the models with large volumes of high-quality image-caption pairs during training. However, recent work suggests that VLMs are vulnerable to stealthy adversarial attacks, where adversarial perturbations are added to images to mislead the VLMs into producing incorrect captions. In this paper, we explore the feasibility of adversarial mislabeling attacks on VLMs as a mechanism to poisoning training pipelines for text-to-image models. Our experiments demonstrate that VLMs are highly vulnerable to adversarial perturbations, allowing attackers to produce benign-looking images that are consistently miscaptioned by the VLM models. This has the effect of injecting strong "dirty-label" poison samples into the training pipeline for text-to-image models, successfully altering their behavior with a small number of poisoned samples. We find that while potential defenses can be effective, they can be targeted and circumvented by adaptive attackers. This suggests a cat-and-mouse game that is likely to reduce the quality of training data and increase the cost of text-to-image model development. Finally, we demonstrate the real-world effectiveness of these attacks, achieving high attack success (over 73%) even in black-box scenarios against commercial VLMs (Google Vertex AI and Microsoft Azure).