Block-based reversible data embedding
Signal Processing
DE-based reversible data hiding with improved overflow location map
IEEE Transactions on Circuits and Systems for Video Technology
Reversible image watermarking using interpolation technique
IEEE Transactions on Information Forensics and Security
Reversible Watermarking Scheme Based on Two-Dimensional Difference Expansion (2D-DE)
ICCRD '10 Proceedings of the 2010 Second International Conference on Computer Research and Development
Embedding capacity raising in reversible data hiding based on prediction of difference expansion
Journal of Systems and Software
High capacity data hiding schemes for medical images based on difference expansion
Journal of Systems and Software
A Novel Cluster-Based Difference Expansion Transform for Lossless Data Hiding
ICGEC '11 Proceedings of the 2011 Fifth International Conference on Genetic and Evolutionary Computing
Histogram-Based Difference Expansion for Reversible Data Hiding with Content Statistics
IIH-MSP '11 Proceedings of the 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing
A Novel Difference Expansion Transform for Reversible Data Embedding
IEEE Transactions on Information Forensics and Security
Difference Expansion Based Reversible Data Hiding Using Two Embedding Directions
IEEE Transactions on Multimedia
Expansion Embedding Techniques for Reversible Watermarking
IEEE Transactions on Image Processing
Reversible data embedding using a difference expansion
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.08 |
For reversible data hiding, the histogram-based difference expansion (DE) is one family of the generic methods where secret bits are embedded into an image by DE on prediction error. However, the ratio of the count of bit 0 to that of bit 1, embedded into the pixels with the same prediction error, will vary with the image and the secret bits, which leads to the embedding unbalance and image distortion. Therefore, a novel hierarchical embedding scheme is proposed to remove the unbalance level by level. We only implement it in two levels considering the complexity of constructing reversible transform for the hierarchical embedding. In the first level, the histogram is divided into different groups according to prediction error, and then two group exchange operations are designed to remove the embedding unbalance depending on the statistical information of this level. For further removing the unbalance, two adjacent groups are combined to a composite group in the second level, and another two composite group exchange operations are constructed to eliminate the unbalance by using the statistical information of the second level. Simulations demonstrate that the proposed hierarchical algorithm can achieve better performance in comparison with other existing histogram-based DE methods.