Colorization Network Watermarking in the CIE-Lab Domain

Abstract

A possible solution to protect the copyright of generative models is to watermark the models so that any image generated by the models contain an invisible watermark, whose presence can be checked at a later stage for ownership verification or to trace back the image to the generator which produced it. In general, a Generative Adversarial Network (GAN) or a diffusion model can be watermarked by applying a frozen pretrained watermark decoder on top of the generator, and adding the watermark decoding loss term to the generator loss. In this paper, we propose a method to watermark image colorization models, that is models whose goal is to introduce plausible colors in grey-level images. The particular color domain wherein colorization models operate requires that the watermark is embedded in the image components that are actually affected by the colorization, avoiding to embed the watermark in the luminance channel, which is usually left unchanged by the colorization process. In particular, we show that the domain the watermark decoder is trained on impacts the performance of the network and better performance in terms of watermark accuracy and robustness can be achieved by training the decoder to extract the watermark bits from the chrominance components in the CIE-Lab space and use the decoder trained in this way to watermark the GAN model.