Text-to-image diffusion models, e.g. Stable Diffusion (SD), lately have shown remarkable ability in high-quality content generation, and become one of the representatives for the recent wave of transformative AI. Nevertheless, such advance comes with an intensifying concern about the misuse of this generative technology, especially for producing copyrighted or NSFW (i.e. not safe for work) images. Although efforts have been made to filter inappropriate images/prompts or remove undesirable concepts/styles via model fine-tuning, the reliability of these safety mechanisms against diversified problematic prompts remains largely unexplored. In this work, we propose Prompting4Debugging (P4D) as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism. We demonstrate the efficacy of our P4D tool in uncovering new vulnerabilities of SD models with safety mechanisms. Particularly, our result shows that around half of prompts in existing safe prompting benchmarks which were originally considered "safe" can actually be manipulated to bypass many deployed safety mechanisms, including concept removal, negative prompt, and safety guidance. Our findings suggest that, without comprehensive testing, the evaluations on limited safe prompting benchmarks can lead to a false sense of safety for text-to-image models.
The core idea behind P4D is to assess the robustness of safety mechanisms by attempting to bypass them. We start with a problematic text prompt \(P\) that typically leads an unconstrained T2I model \(\mathcal{G}\) to generate images containing an inappropriate concept/object \(\mathcal{C}\). We then feed this prompt \(P\) to another T2I model \(\mathcal{G}'\) equipped with a safety mechanism designed to prevent the generation of \(\mathcal{C}\). The goal is to counteract the safety
mechanism of \(\mathcal{G}'\) such that the inappropriate concept/object \(\mathcal{C}\) now again appears in the generated image.
To achieve this, P4D employs prompt engineering to optimize a new or modified prompt \(P^\ast\) for \(\mathcal{G}'\) such that it produces content similar to that generated by the unconstrained T2I model when conditioned on \(P\). The optimization process takes place in the latent space and involves matching noise predictions between \(\mathcal{G}\) and \(\mathcal{G}'\). Notably, we experiment with two variants of \(P^\ast\): P4D-\(N\) and P4D-\(K\), with the latter offering better adaptability to the length of the original prompt \(P\). This red-teaming tool helps assess the reliability of safety mechanisms in T2I models and identify potential vulnerabilities, offering valuable insights into model security and performance.
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@inproceedings{chin2023prompting4debugging, title={Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts}, author={Zhi-Yi Chin and Chieh-Ming Jiang and Ching-Chun Huang and Pin-Yu Chen and Wei-Chen Chiu}, booktitle={International Conference on Machine Learning (ICML)}, year={2024}, url={https://arxiv.org/abs/2309.06135} }