Hiding Images in Diffusion Models by Editing Learned Score Functions

Hiding data in deep neural networks (DNNs) has achieved remarkable successes, including both discriminative and generative models. Yet, the potential for hiding images in diffusion models remains underdeveloped. Existing approaches fall short in extracting fidelity, secrecy, and efficiency. In particular, the intensive computational demands of the hiding process, coupled with the slow extraction due to multiple denoising stages, make these methods impractical for resource-limited environments. To address these challenges, we propose hiding images at a specific denoising stage in diffusion models by modifying the learned score functions. We also introduce a parameter-efficient fine-tuning (PEFT) approach that combines parameter selection with a variant of low-rank adaptation (LoRA) to boost secrecy and hiding efficiency. Comprehensive experiments demonstrate the effectiveness of our proposed method.