WaterDiff: Perceptual Image Watermarks Via Diffusion Model
Yuqi Tan, Yuang Peng, Hao Fang, Bin Chen, Shu‐Tao Xia
Abstract
Recent studies have demonstrated that diffusion probabilistic models (DPMs) have numerous advantages in image generation through learning a decodable latent representation. This characteristic makes DPMs an appropriate reversible model for encoding and decoding of image watermarking. We present WaterDiff, which leverages pretrained DPMs for perceptual image watermarking problem. Specifically, WaterDiff embeds the watermark into the decomposed stochastic feature, then the stochastic features is combined with the corresponding semantic latent vector to produce a watermarked image via DPMs. This process balances the perceptual quality (stealthiness) and watermarking capacity by fully exploiting the latent diffusion prior. Extensive experiments indicate that WaterDiff guarantee both perceptual imperceptibility and robustness against state-of-the-art watermarking attacks.