This invention discloses a lightweight watermarking method for robust image attribution based on latent space
diffusion models. The method uses low-dimensional feature vectors as the
watermark carrier: first, a feature
watermark library is constructed; then, a feature-
noise encoder and a
noise-feature decoder are trained. The
encoder maps the feature
watermark to initial latent space
noise that satisfies a standard
Gaussian distribution and inputs it into the latent space
diffusion model to generate an image. When attribution
verification is required, the image to be verified is inverted to recover the initial latent space noise, and the corresponding feature watermark is reconstructed by the decoder. The identity is determined using the
cosine similarity between the reconstructed watermark and the original watermark in the feature watermark
library. This invention maintains the quality of the generated image while exhibiting strong robustness to common distortions such as
JPEG compression,
cropping, random discarding, blurring, and brightness variations, and significantly reduces the overhead of watermark storage and matching computation.