[Volume 1, Issue 1] 6. Recent advances in predictive models for reversible data hiding: a contemporary survey
2021-03-11 Xiao-zhu Xie, and Ching-Chun Chang* 


Reversible data hiding (RDH) is a technique that permits marked carriers to be restored to their pristine state after authenticated by erasing the embedding distortion. It has become a research hotspot in recent years owing to the growing concerns about privacy in cloud computing environments. While plain images have been the major carriers, encrypted images have emerged as a new type of carriers in response to the privacy and security issues. Regardless of whether plain or encrypted images are used as the carrier, prediction techniques play an important role in improving the performance of RHD schemes. In the present study, we review the following widely used prediction techniques: 1) neighboring- pixel predictor, 2) block-based single-reference predictor, 3) block-based multi-reference predictor, 4) checkerboard-based predictor, 5) median edge detection (MED), 6) gradient adjusted prediction (GAP), 7) simple gradient adjusted prediction (SGAP), and 8) most significant bit (MSB) prediction. For each predictor, we discuss its algorithm, its use in RDH schemes, its merits and demerits. It is expected that the prediction mechanisms will keep moving forward and result in further gains in the RDH.


Reversible data hiding, Checkerboard-based, Median edge detection, Gradient adjusted prediction, Simple gradient adjusted prediction, MSB prediction.

Authors and contacts:

Xiao-zhu Xie, Department of Computer Science, Xiamen University of Technology, Xiamen 361024, China; Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, China, xz4xxz@gmail.com

Ching-Chun Chang*, Department of Computer Science, University of Warwick, Coventry CV4 7AL, U.K., ching-chun.chang@warwickgrad.net


Xiao-zhu Xie, Ching-Chun Chang, “Recent advances in predictive models for reversible data hiding: a contemporary survey”, Journal of Computer Security and Data Forensics, Vol. 1, No. 1, pp. 77~90, March 2021.