Recently, various types of Text-to-Image (T2I) models have emerged (such as DALL-E and Stable Diffusion), and showing their advantages in different aspects. Therefore, some third-party service platforms collect different model interfaces and provide cheaper API services and more flexibility in T2I model selections. However, this also raises a new security concern: Are these third-party services truly offering the models they claim? To answer this question, we first define the concept of T2I model verification, which aims to determine whether a black-box target model is identical to a given white-box reference T2I model. After that, we propose VerifyPrompt, which performs T2I model verification through a special designed verify prompt. Intuitionally, the verify prompt is an adversarial prompt for the target model without transferability for other models. It makes the target model generate a specific image while making other models produce entirely different images. Specifically, VerifyPrompt utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize the cosine similarity of a prompt's text encoding, generating verify prompts. Finally, by computing the CLIP-text similarity scores between the prompts the generated images, VerifyPrompt can determine whether the target model aligns with the reference model. Experimental results demonstrate that VerifyPrompt consistently achieves over 90\% accuracy across various T2I models, confirming its effectiveness in practical model platforms (such as Hugging Face).
@article{arxiv.2410.22725,
title = {One Prompt to Verify Your Models: Black-Box Text-to-Image Models Verification via Non-Transferable Adversarial Attacks},
author = {Ji Guo and Wenbo Jiang and Rui Zhang and Guoming Lu and Hongwei Li},
journal= {arXiv preprint arXiv:2410.22725},
year = {2025}
}