Intra-Block Correlation Based Reversible Data Hiding in Encrypted Images Using Parametric Binary Tree Labeling

Symmetry

Rajeev Kumar¹  Hari Om²  Arun Kumar Rai²  Ki-Hyun Jung³  Neeraj Kumar⁴  Satish Chand⁵ 

[1] Department of Computer Science and Engineering, Delhi Technological University, Delhi 110042, India;Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India;Department of Cyber Security, Kyungil University, Kyeongbuk 38424, Korea;Department of Electronics and Communication Engineering, Nagarjuna College of Engineering and Technology, Bengaluru 562164, India;School of Computer and System Sciences, Jawaharlal Nehru University, Delhi 110067, India;

关键词: image encryption;    reversible data hiding;    parametric binary tree labeling;    privacy;    intra-block correlation;   

DOI  :  10.3390/sym13061072

来源: DOAJ

【 摘 要 】

In this paper, a high capacity reversible data hiding technique using a parametric binary tree labeling scheme is proposed. The proposed parametric binary tree labeling scheme is used to label a plaintext image’s pixels as two different categories, regular pixels and irregular pixels, through a symmetric or asymmetric process. Regular pixels are only utilized for secret payload embedding whereas irregular pixels are not utilized. The proposed technique efficiently exploits intra-block correlation, based on the prediction mean of the block by symmetry or asymmetry. Further, the proposed method utilizes blocks that are selected for their pixel correlation rather than exploiting all the blocks for secret payload embedding. In addition, the proposed scheme enhances the encryption performance by employing standard encryption techniques, unlike other block based reversible data hiding in encrypted images. Experimental results show that the proposed technique maximizes the embedding rate in comparison to state-of-the-art reversible data hiding in encrypted images, while preserving privacy of the original contents.

【 授权许可】

Unknown