Robust medical zero-watermarking algorithm based on Residual-DenseNet

Abstract

To solve the problem of poor robustness of existing traditional DCT-based medical image watermarking algorithms under geometric attacks, a novel deep learning-based robust zero-watermarking algorithm for medical images is proposed. A Residual-DenseNet is designed, which took low-frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high-level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks.