ConvNet-HIDE: Deep Learning-Based Dual Watermarking for Healthcare Images

Recently, significant attention has been focused on copyright protection of medical images, causing watermarking to become a prevalent research topic. In comparison to embedding a single watermark directly into cover images, inserting multiple marks through a deep neural network enables image authentication and verification of ownership, which is more suitable for healthcare applications. Most of the existing methods have proposed watermarking through insertion of a single mark in a deep-learning environment, which does not consider an excellent relationship between robustness, imperceptibility and capacity and therefore limits watermarking performance. In this paper, we propose a new watermarking method based on a convolutional neural network (ConvNet) called ConvNet- HIDE as well as principal component analysis (PCA). First, the customized UNet3+ model is applied to segment the region of interest (ROI) and background information of the image. Then, the ConvNet model is used with Mobi-ConvNet, a downsampler, to imperceptibly hide a mark in both segmented parts of the image. Finally, the ConvNet model, which is followed by PCA, is used to recover both marks robustly. Extensive experimental results demonstrate the effectiveness of our proposed ConvNetbased method compared to state-of-the-art schemes. The method is able to achieve PSNR and SSIM as 39.59 dB and 0.9812 with capacity of 2 bpp. Further, the average execution time for our method is found to be 0.22 seconds, demonstrating its efficiency compared to other similar techniques that require more time.