Review: A review on blind detection for image steganography
Signal Processing
ISC '08 Proceedings of the 11th international conference on Information Security
Steganalysis of LSB matching based on statistical modeling of pixel difference distributions
Information Sciences: an International Journal
Passive steganalysis based on higher order image statistics of curvelet transform
International Journal of Automation and Computing
Steganalysis of LSB matching based on the statistical analysis of empirical matrix
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Multimedia Tools and Applications
Parameter-estimation and algorithm-selection based United-Judgment for image steganalysis
Multimedia Tools and Applications
Steganalysis based on differential statistics
CANS'06 Proceedings of the 5th international conference on Cryptology and Network Security
Pixel rearrangement based statistical restoration scheme reducing embedding noise
Multimedia Tools and Applications
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In this paper, an effective steganalysis based on statistical moments of wavelet characteristic function is proposed. It decomposes the test image using twolevel Haar wavelet transform into nine subbands (here the image itself is considered as the LL0 subband). For each subband, the characteristic function is calculated. The first and second statistical moments of the characteristic functions from all the subbands are selected to form an 18-dimensional feature vector for steganalysis. The Bayes classifier is utilized in classification. All of the 1096 images from the CorelDraw image database are used in our extensive experimental work. With randomly selected 100 images for training and the remaining 996 images for testing, the proposed steganalysis system can steadily achieve a correct classification rate of 79% for the non-blind Spread Spectrum watermarking algorithm proposed by Cox et al., 88% for the blind Spread Spectrum watermarking algorithm proposed by Piva et al., and 91% for a generic LSB embedding method, thus indicating significant advancement in steganalysis.