Steganalysis of LSB matching in grayscale images

By Andrew D. Ker. IEEE Signal Processing Letters, 2005

635 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/47d5ae8afe0b8387aa12fd36393336c4c2439906

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In Computer Vision and Pattern Recognition

1. Jessica J. Fridrich
2. Bin Li
3. Jiwu Huang
4. Tomás Pevný
5. Vojtech Holub
6. Y. Shi
7. Tomáš Filler
8. Jan Kodovský

Citations in the corpus

• Benchmarking for Steganography (2008, background): “), and a functional assigning a single numerical value to the ROC curve (total minimal decision error [16, 38], probability of detection for fixed false alarm rate [26], or false alarm for probability of detection 50% [20], accuracy [13], etc.”
• Gibbs Construction in Steganography (2010, background): “In LSB matching [16] (also called 1 embedding) in an 8-bit grayscale image , whenever and is appropriately modified for the boundary cases.”
• Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images (2008, neutral): “HCFCOM) [11].”
• Matrix embedding for large payloads (2006, background): “This is a significant improvement in view of the fact that the performance of current steganalyzers for some embedding methods may be quite sensitive to the relative payload in this range (see, for example, [6] and [7]).”
• Steganalysis of LSB matching using differences between nonadjacent pixels (2016, methodology): “COM [18] is a special detection method which extracts only one feature.”
• Efficient Steganographic Embedding by Exploiting Modification Direction (2006, result): “compared to a plain LSB matching technique, therefore has improved security against the HCF-COM analysis introduced in [9] and developed in [10].”
• A Survey on Image Steganography and Steganalysis (2011, methodology): “Ker [46] proposed an effective scheme for estimating the statistics of spatial cover images via down-sampling.”
• Edge Adaptive Image Steganography Based on LSB Matching Revisited (2010, methodology): “The experimental results in [10] shows that the method outperforms the previous calibrated HCF COM methods in [8].”
• Steganalysis of least significant bit matching using multi-order differences (2014, methodology): “Ker improved the aforementioned method by calculating feature from the adjacency histogram instead of the usual histogram and using the calibration mechanism [6].”