A Novel Difference Expansion Transform for Reversible Data Embedding

By Hyoung-Joong Kim, Vasiliy Sachnev, Y. Shi, Jeho Nam, Hyon‐Gon Choo. IEEE Transactions on Information Forensics and Security, 2008

383 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d084687dd8b85504673fd90165e0e31624808099

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiaolong Li
2. Bin Yang
3. Weiming Zhang
4. Xinlu Gui
5. Y. Shi
6. Dinu Coltuc
7. Wei-Liang Tai
8. Tung-Shou Chen

Citations in the corpus

• Reversible Data Hiding Based on Histogram Modification of Pixel Differences (2009, background): “Laplacian data can be applied to data hiding schemes [22]–[24] to improve their embedding ability.”
• Reversible data hiding for high quality images using modification of prediction errors (2009, positive): “The first approach under this category was proposed by Tian (Tian, 2003), and extended by many recent researches (Alattar, 2004; Kamstra and Heijmans, 2005; Thodi and Rodriguez, 2007; Kim et al., 2008).”
• Efficient Reversible Data Hiding Based on Multiple Histograms Modification (2015, methodology): “Afterwards, the expansion technique has been widely investigated and developed, mainly in the aspects of integer-to-integer transformation [18]–[22], location map reduction [23]–[25], and prediction-error expansion (PEE) [26]–[35].”
• High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation (2016, methodology): “Many RDH algorithms have already been developed, such as image compression-based [1], [2], difference expansionbased [3]–[7], histogram shift (HS)-based [8]–[11], image pixel pair based [12], [13], and dual/multi-image [14], [15] hiding methods.”
• Reversible Image Watermarking Using Interpolation Technique (2010, neutral): “Once the same (cid:3)(cid:4) , (cid:3)(cid:10) , (cid:5)(cid:4) , and (cid:5)(cid:10) are known, embedded data can be extracted through (cid:8) (cid:0) (cid:19) (cid:9) (cid:0) (cid:0) (cid:0) (cid:3)(cid:4) (cid:15)(cid”
• Reversible Watermarking Algorithm Using Sorting and Prediction (2009, methodology): “[8] improved the difference expansion methods [7], [15] by using a novel simplified location map approach.”