Dark Miner: Defend against unsafe generation for text-to-image diffusion models

Zheling Meng, Bo Peng, Xiaochuan Jin, Yue Jiang, Jing Dong, Wei Wang, Tieniu Tan

Keywords: Text-to-Image Diffusion Models, Unsafe Generation, Concept Erasure

TL;DR: A new method to erase unsafe concepts and defend against attacks for text-to-image diffusion models.

Abstract: Text-to-image diffusion models have been demonstrated with unsafe generation due to unfiltered large-scale training data, such as violent, sexual, and shocking images, necessitating the erasure of unsafe concepts. Most existing methods focus on modifying the generation probabilities conditioned on the texts containing unsafe descriptions. However, they fail to guarantee safe generation for unseen texts in the training phase, especially for the prompts from adversarial attacks. In this paper, we re-analyze the erasure task and point out that existing methods cannot guarantee the minimization of the total probabilities of unsafe generation. To tackle this problem, we propose Dark Miner. It entails a recurring three-stage process that comprises mining, verifying, and circumventing. It greedily mines embeddings with maximum generation probabilities of unsafe concepts and reduces unsafe generation more effectively. In the experiments, we evaluate its performance on two inappropriate concepts, two objects, and two styles. Compared with 6 previous state-of-the-art methods, our method achieves better erasure and defense results in most cases, especially under 4 state-of-the-art attacks, while preserving the model's native generation capability. Our code can be found in Supplementary Material and will be available on GitHub.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 5485