Matrix embedding for large payloads

By Jessica J. Fridrich, David Soukal. IEEE Transactions on Information Forensics and Security, 2006

333 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9016da3b833ba858e85bf814b9dd51e27aff6998

How researchers see this paper

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

In Artificial Intelligence

1. Bin Li
2. Y. Shi
3. Jiwu Huang
4. Junhui He
5. Jessica J. Fridrich
6. Shuozhong Wang
7. Xinpeng Zhang
8. Tomás Pevný

In Computational Theory and Mathematics

1. Shuozhong Wang
2. Xinpeng Zhang
3. Jessica J. Fridrich
4. Jan Judas
5. Tomáš Filler

In Computer Vision and Pattern Recognition

1. Jessica J. Fridrich
2. Bin Li
3. Jiwu Huang
4. Y. Shi
5. Vojtech Holub
6. Tomáš Filler
7. Jan Judas
8. Junhui He

Citations in the corpus

• Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes (2011, background): “Later on, different authors suggested oth er linear codes, such as Golay [30], BCH [31], random codes of small dimension [32], and non-linear codes based on the idea of a blockwise direct sum [29].”
• Efficient Steganographic Embedding by Exploiting Modification Direction (2006, methodology): “In [6], two steganographic encoding methods based on random linear”
• A Survey on Image Steganography and Steganalysis (2011, background): “Fridrich et al. [76] proposed to use random linear codes or simplex codes to cope with the case when the embedding rate is high.”