Hiding Images in Diffusion Models by Editing Learned Score Functions
Journal:
arXiv
Published Date:
Mar 24, 2025
Abstract
Hiding data using neural networks (i.e., neural steganography) has achieved
remarkable success across both discriminative classifiers and generative
adversarial networks. However, the potential of data hiding in diffusion models
remains relatively unexplored. Current methods exhibit limitations in achieving
high extraction accuracy, model fidelity, and hiding efficiency due primarily
to the entanglement of the hiding and extraction processes with multiple
denoising diffusion steps. To address these, we describe a simple yet effective
approach that embeds images at specific timesteps in the reverse diffusion
process by editing the learned score functions. Additionally, we introduce a
parameter-efficient fine-tuning method that combines gradient-based parameter
selection with low-rank adaptation to enhance model fidelity and hiding
efficiency. Comprehensive experiments demonstrate that our method extracts
high-quality images at human-indistinguishable levels, replicates the original
model behaviors at both sample and population levels, and embeds images orders
of magnitude faster than prior methods. Besides, our method naturally supports
multi-recipient scenarios through independent extraction channels.
