Patronus: Safeguarding Text-to-Image Models Against Adversarial Fine-Tuning
Text-to-image (T2I) models can be exploited to produce unsafe images. Existing safety measures, e.g., content moderation or model alignment, can be weakened by adversaries who attempt to restore unsafe generation through model fine-tuning. This paper presents Patronus, a defensive framework that improves T2I models’ resistance to the gradient-based adversarial fine-tuning attacks evaluated in this work. Specifically, we design a co-trained safety decoder that produces a deliberately corrupted output for a latent representation associated with unsafe content while preserving normal decoding for benign content. We also strengthen the decoder and U-Net with a non-fine-tunable learning mechanism. Across I2P, SneakyPrompt, and MMA-Diffusion, Patronus obtains attack success rates of 0.01–0.03 and true positive rates of 0.98–0.99. On benign prompts, it obtains FID 23.6, LPIPS 0.78, and a false positive rate of 0.01. The fine-tuning stress tests separately report the optimization losses of the defended decoder and U-Net under the evaluated attack settings.