New blind steganalysis and its implications

By Miroslav Goljan, Jessica J. Fridrich, Taras Holotyak. Electronic imaging, 2006

305 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d851222f5875e7f5534bc5f1184030be852cc3df

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. Vojtech Holub
3. Bin Li
4. Jan Kodovský
5. Jiwu Huang
6. Tomás Pevný
7. Jiangqun Ni
8. Yang Yi

In Signal Processing

1. Jan Kodovský
2. Jiwu Huang
3. Patrick Bas
4. Tomás Pevný
5. Fangjun Huang
6. Jiangqun Ni
7. Yang Yi
8. Jian Ye

Citations in the corpus

• Steganalysis of least significant bit matching using multi-order differences (2014, background): “extracted higher-order statistic features from the wavelet coefficients to train classifier [9].”
• Rich Models for Steganalysis of Digital Images (2012, methodology): “Personal use of this material is permitted.”
• Edge Adaptive Image Steganography Based on LSB Matching Revisited (2010, methodology): “lytic algorithms such as [11], [12], and [13] can also be used for exposing the stego images using LSBM and/or other steganographic methods with a relatively high detection accuracy.”
• Ensemble Classifiers for Steganalysis of Digital Media (2012, methodology): “…used only a few dozen features, e.g., 72 higher order moments of coefficients obtained by transforming an image using quadratic mirror filters [14], 18 binary similarity metrics [3], 23 discrete cosine transform (DCT) features [17], and 27 higher order moments of wavelet coefficients [21].”
• Steganalysis by Subtractive Pixel Adjacency Matrix (2009, methodology): “It is, however, still impossible to model the differences using a Markov chain as the transition probability matrix would have 511 2 elements.”
• A Survey on Image Steganography and Steganalysis (2011, methodology): “Goljan et al. [69] contributed a method with features from the first nine absolute central moments of the PDF (thereafter absolute PDF moments) of the estimated stego-noise.”
• Deep Learning Hierarchical Representations for Image Steganalysis (2017, methodology): “In practice, many steganalysis schemes [17], [19] implement the predictor by convolving a finite impulse response filter K ′ with image X :”