LSB matching revisited

By J. Mielikainen. IEEE Signal Processing Letters, 2006

1,069 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fa940bb75c1ac4d5e250d0a143bc213f87d4ec8d

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. Y. Shi
2. Bin Li
3. Jiwu Huang
4. Shuozhong Wang
5. Fangjun Huang
6. Junhui He
7. Xinpeng Zhang
8. George Danezis

Citations in the corpus

• Generating steganographic images via adversarial training (2017, methodology): “However, information from the original image is always lost when using the LSB technique, and is known to be vulnerable to steganalysis [6].”
• A Survey on Image Steganography and Steganalysis (2011, methodology): “In order to avoid PoV statistical attack, LSB matching[18, 19, 20], which is a minor modification of LSB steganography, is proposed.”
• Efficient Steganographic Embedding by Exploiting Modification Direction (2006, methodology): “Performance comparison has been made among the proposed EMD method, the running coding [7], the matrix encoding [4], and the method described in [8] as shown in Fig.”
• Edge Adaptive Image Steganography Based on LSB Matching Revisited (2010, methodology): “pixel values independently, LSB matching revisited (LSBMR) [1] uses a pair of pixels as an embedding unit, in which the LSB of the first pixel carries one bit of secret message, and the relationship (odd–even combination) of the two pixel values car-”