Steganalysis of additive-noise modelable information hiding

By J. H. Harmsen, William A. Pearlman. IS&T/SPIE Electronic Imaging, 2003

473 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f9462f0d14e5f53f2465815c8d568f134916f0a0

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

Citations in the corpus

• Rich Models for Steganalysis of Digital Images (2012, background): “The model is usually determined not only by the characteristics of the cover source but also by the effects of embedding [4], [6], [14], [18], [21], [33], [35], [38].”
• Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images (2008, methodology): “We also compare A. HCFCOM and C.A. HCFCOM proposed by Ker [11].”
• Steganalysis of least significant bit matching using multi-order differences (2014, methodology): “They calculated the center of mass of histogram characteristic function as the feature [5].”
• A Survey on Image Steganography and Steganalysis (2011, background): “As mentioned in Section 4.1.2, host-independent additive noise has a low-pass filtering effect on the PMF of the image [45].”
• 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].”
• Edge Adaptive Image Steganography Based on LSB Matching Revisited (2010, methodology): “, [7]–[10]) have been proposed to analyze the LSBM scheme.”
• An improved approach to steganalysis of JPEG images (2010, methodology): “To date, a few steganographic methods such as LSB embedding/matching and spread spectrum steganography [5,6,8,9,13,15,16,18–21] have been successfully steganalyzed.”
• New blind steganalysis and its implications (2006, methodology): “Harmsen et al.(8) used a simple three-dimensional feature vector obtained as the center of gravity of the three-dimensional Histogram Characteristic Function (HCF).”
• Merging Markov and DCT features for multi-class JPEG steganalysis (2007, background): “13,17,23 For best results, the features for steganalysis should react sensitively to embedding changes but be otherwise insensitive to image content.”
• Steganalysis of LSB matching using differences between nonadjacent pixels (2016, methodology): “Harmsen and Pearlman [15] considered message embedding as the disturbance of additive noise, and calculated the center of mass of histogram characteristic function (HCF COM) to detect the hidden message.”
• Steganalysis by Subtractive Pixel Adjacency Matrix (2009, methodology): “One of the first heuristic detectors of embedding by noise adding used the center of gravity of the Histogram Characteristic Function [11], [17], [26] (HCF).”
• LSB matching revisited (2006, methodology): “The best-known detector for LSB matching is based on the center of mass (COM) of the histogram characteristic function (HCF) introduced by Harmsen et al. [4].”