Lite Localization Network and DUE-Based Watermarking for Color Image Copyright Protection

Deep learning-based watermarking frameworks have received extensive research attention in recent years. The main structure of this framework consists of an encoder, a noise layer and a decoder (Encoder-NoiseLayer-Decoder). However, such a framework has the major drawback that it requires visible markers to locate a watermarked image, which compromises the imperceptibility of watermarking. To address this restriction, a novel Lite localization network based on Lite-HRNet is proposed. In order to generate high-quality watermarked image, we designed the Double U-Net Encoder (DUE), which can better hide the watermarking information in image pixels that are invisible to the human eye. Meanwhile, to improve robustness, two bicubic interpolation operations are added to the noise layer to increase the type of distortion. In addition, to further enhance the performance of the watermarking algorithm, the novel WGAN-GP loss function based on discriminator is designed to guide the training of the model. Numerous experiments demonstrate the superior performance of our proposed scheme in terms of localization function, visual quality, and robustness. The proposed scheme shows better results compared to state-of-the-art algorithms.