Affiliation:
1. Department of Software Convergence, Andong National University, Andong 36729, Republic of Korea
Abstract
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.
Funder
Andong National University
Reference20 articles.
1. CNN Prediction Based Reversible Data Hiding;Hu;IEEE Signal Process. Lett.,2011
2. Reversible data embedding using a difference expansion;Tian;IEEE Trans. Circuits Syst. Video Technol.,2003
3. Difference expansion based reversible data embedding and edge detection;Gujjunoori;Multimed. Tools Appl.,2019
4. Efficient Reversible Data Hiding Based on Multiple Histograms Modification;Li;IEEE Trans. Inf. Forensics Secur.,2015
5. Thodi, D.M., and Rodriguez, J.J. (2004, January 28–30). Reversible watermarking by prediction-error expansion. Proceedings of the 6th IEEE Southwest Symposium on Image Analysis and Interpretation, Lake Tahoe, NV, USA.