Cross-domain convolutional neural network predictor-based reversible data hiding using masking strategy

We propose a cross-domain convolutional neural network (CNN) predictor-based reversible data hiding (RDH) using a masking strategy. The proposed cross-domain predictor fuses the dual features of the image’s spatial and frequency domains, which enhances the ability to simultaneously control both local and global features of the image. To the best of our knowledge, we are the first to report in detail on how to use cross-domain-based CNN to predict images for RDH. Furthermore, we develop the masking strategy to create a new training loss that ensures complete control over the predicted part. The experimental results show that the prediction error of the cross-domain predictor conforms very well to the Gaussian distribution, and the average number of zeros on the measured dataset is as high as 42,579. The peak signal-to-noise ratio (PSNR) value of the hidden boat image is able to reach 64.71 dB, and the average PSNR is also able to be above 51.63 dB in the case of a very large capacity (80,000 bits). Under different capacities, the average structural similarity index measure, universal quality index, and visual information fidelity values are as high as 0.9978, 0.9999, and 0.9736, respectively. This proves that the proposed cross-domain predictor outperforms the state-of-the-art predictors and generates hidden images with superior visual quality.