Invertible authentication

By Jessica J. Fridrich, Miroslav Goljan, Rui Du. Security and Watermarking of Multimedia Contents, 2001

507 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1f26321ea29a2c1e7f2e64a428ea16c67dce51ce

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. Wei-Liang Tai
5. Chin-chen Chang
6. Y. Shi
7. Xinpeng Zhang
8. Dinu Coltuc

Citations in the corpus

• Reversible Data Hiding Based on Histogram Modification of Pixel Differences (2009, methodology): “[3], [4] devised an invertible watermarking method by using a lossless compression algorithm to make space in which to embed data.”
• Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection (2011, background): “…remote sensing, military image processing, medical image sharing, multimedia archive management, etc. Early reversible watermarking algorithms are mainly based on lossless compression [4]–[8], in which certain features of host image are losslessly compressed to save space for embedding the payload.”
• Lossless watermarking for image authentication: a new framework and an implementation (2006, background): “Note that none of the lossless authentication methods in the literature offer tamper localization capability, which is one of the major advantages of authentication watermarks over conventional digital signatures.”
• Lossless generalized-LSB data embedding (2005, methodology): “Early work [10], [11] on Type-II lossless embedding proposed compressing one of the LSB planes of the image by either using a bi-level lossless image compression algorithm, e.”
• Efficient Reversible Data Hiding Based on Multiple Histograms Modification (2015, methodology): “Early RDH methods are mainly based on lossless compression [11]–[14].”
• Statistical Tools for Digital Forensics (2004, background): “Within this broad area, several authentication schemes have been proposed: embedded signatures [10, 24, 25, 18, 2], erasable fragile watermarks [11, 9], semi-fragile watermarks [16, 23, 28, 15], robust tell-tale watermarks [27, 14, 28], and self-embedding watermarks [8].”
• Expansion Embedding Techniques for Reversible Watermarking (2007, background): “proposed a generalized LSB (g-LSB) embedding algorithm [4], extending the work in [9] and, thus, were able to introduce several additional points along the capacity-distortion curve.”
• Reversible data hiding (2003, background): “Overall comparison between other reversible marking methods [10,11,13,14,16,17] and our proposed method.”
• Reversible data hiding: Advances in the past two decades (2016, methodology): “Some other initial RDH schemes, either for fragile authentication or high capacity, are based on lossless compression [16]–[24].”
• Reversible data hiding (2006, background): “Since these techniques [10], [11], [13] aim at authentication, the amount of hidden data is limited.”
• Exposing digital forgeries by detecting traces of resampling (2005, background): “Within this broad area, several authentication schemes have been proposed: embedded signatures [3], [4], [5], [6], [7], eraseable fragile watermarks [8], [9], semi-fragile watermarks [10], [11], [12], [13], robust tell-tale watermarks [14], [15], [12], [16], [17], and self-embedding watermarks [18].”
• DE-Based Reversible Data Hiding With Improved Overflow Location Map (2009, methodology): “[2], [6] proposed several methods to embed data.”
• Circular interpretation of bijective transformations in lossless watermarking for media asset management (2003, background): “[18] propose to replace a bit plane of the image to be watermarked.”
• Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding (2013, background): “, compression based algorithms [7]–[11], difference expansion (DE) based algorithms [12]–[16], histogram shifting (HS) based algorithms [17]–[19], prediction-error expansion (PEE) based algorithms [20]–[34] and integer-to-integer transform based algorithms [35]–[41], etc.”