Learnable Color Image Zero-Watermarking Based on Feature Comparison

Zero-watermarking is one of the solutions to protect the copyright of color images without tampering with them. Existing zero-watermarking algorithms either rely on static classical techniques or employ pre-trained models of deep learning, which limit the adaptability of zero-watermarking to complex and dynamic environments. These algorithms are prone to fail when encountering novel or complex noise. To address this issue, we propose a self-supervised anti-noise learning color image zero-watermarking method that leverages feature matching to achieve lossless protection of images. In our method, we use a learnable feature extractor and a baseline feature extractor to compare the features extracted by both. Moreover, we introduce a combined weighted noise layer to enhance the robustness against combined noise attacks. Extensive experiments show that our method outperforms other methods in terms of effectiveness and efficiency.