A New Reversible Data Hiding Method Using a Proportional Relation between Peak Signal-to-Noise Ratio and Embedding Capacity on Convolutional Neural Network
Yong-Yeol Bae, Dae-Jea Cho, Ki‐Hyun Jung · Applied Sciences · 2024
In the rapidly evolving fields of artificial intelligence and various industries, the secure processing and management of massive data have become paramount. This paper introduces an innovative reversible data hiding (RDH) method that leverages a Convolutional Neural Network (CNN)-based predictor to generate a predicted image from a given cover image. The secret data are ingeniously embedded within the differences in pixel values between the cover and predicted images. Our experimental analysis reveals a notable reduction in image distortion with increasing secret data size, showcasing the method’s potential for diverse applications. The unique aspect of our approach lies in the proportional relation between the Peak Signal-to-Noise Ratio (PSNR) and Embedding Capacity, highlighting its efficacy and efficiency in reversible data hiding.