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Mathematical Problems in Engineering
Volume 2015, Article ID 670535, 12 pages
Research Article

Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

1School of Information Science & Technology, Jiujiang University, Jiujiang 332005, China
2Institute of Network & Information Security, Jiujiang University, Jiujiang 332005, China

Received 3 May 2015; Revised 29 June 2015; Accepted 30 July 2015

Academic Editor: Roque J. Saltarén

Copyright © 2015 Guangyong Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE) technique. Firstly, the extreme learning machine (ELM) with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED) predictor and gradient-adjusted predictor (GAP), the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches.