Circular interpretation of bijective transformations in lossless watermarking for media asset management

By C. De Vleeschouwer, J. Delaigle, Benoit M. Macq. IEEE Trans. Multim., 2003

463 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/85b5226b21fa767e8c87846f4402330ff76aceaf

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Reversible Data Hiding Based on Histogram Modification of Pixel Differences (2009, methodology): “[5] proposed a semi-fragile technique based on the patchwork algorithm and modulo operation.”
Improved Embedding for Prediction-Based Reversible Watermarking (2011, background): “[27] divides disjoint image blocks in two equal size regions randomly selected and embeds 1 bit per block by circularly shifting the histogram of one region.”
Reversible data hiding: Advances in the past two decades (2016, background): “[122], [123], which is based on the correlations existed among the neighboring pixels.”
Reversible data hiding (2003, background): “From the above investigation, it can be concluded that all reversible data hiding algorithms based on modulo-256 addition to avoid overflow and underflow, say, in [5] and [20] cannot be applied to many real applications, and hence should be avoided.”
DE-Based Reversible Data Hiding With Improved Overflow Location Map (2009, methodology): “[5] proposed a circular histogram scheme and used the”
Reversible data embedding into images using wavelet techniques and sorting (2005, methodology): “Vleeschouwer et al. [12] propose a technique for reversible data embedding that is based on a combination of the “classical” patchwork algorithm [1] and the concept of a circular histogram.”
Reversible data embedding using a difference expansion (2003, background): “[6], propose a reversible data-embedding algorithm by circular interpretation of bijective transformations.”
Lossless generalized-LSB data embedding (2005, background): “In Type-II algorithms [8], [9], [10], [11], information bits are embedded by modifying, e.g. overwriting, selected features (portions) of the host signal—for instance least significant bits, high frequency wavelet coefficients.”
Expansion Embedding Techniques for Reversible Watermarking (2007, methodology): “A very different approach was proposed by De Vleeschouwer et al. [ 5 ] based on the circular interpretation of the bijective transformations of the image histograms to reduce the salt-and-pepper visual artifacts found in the type-I algorithms.”
Very Fast Watermarking by Reversible Contrast Mapping (2007, background): “There are some watermarking schemes that do not rely on additional data compression, as for instance, the circular histogram interpretation schemes [5], but they have the drawback of a low embedding capacity.”
Reversible Image Watermarking Based on Integer-to-Integer Wavelet Transform (2007, background): “proposed a lossless watermarking based on circular interpretation of bijective transformations [11].”
A Novel Difference Expansion Transform for Reversible Data Embedding (2008, methodology): “Among many reversible data-embedding algorithms [1]–[ 15 ], [17]–[22], Tian’s method [14] and Kamstra and”
Multilevel reversible data hiding based on histogram modification of difference images (2008, methodology): “[11] proposed a lossless watermarking algorithm that relies on circular interpretation of bijective transformations.”