TY - GEN
T1 - Compact image steganalysis for LSB-matching steganography
AU - Juarez-Sandoval, Oswaldo
AU - Cedillo-Hernandez, Manuel
AU - Sanchez-Perez, Gabriel
AU - Toscano-Medina, Karina
AU - Perez-Meana, Hector
AU - Nakano-Miyatake, Mariko
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/26
Y1 - 2017/5/26
N2 - In this paper, we propose a compact image steganalysis method for the LSB-matching steganography, in which a feature vector composed by only 12 elements is extracted from the image. We analyze the statistical artifact occurred in images when a secret data is embedded in it by the LSB-matching steganography. We selected 12 most relevant features based on the probability density function (PDF) of difference of adjacent pixels and the co-occurrence matrix of the image, which can distinguish stegoimages from the natural images. The Support Vector Machine (SVM) is employed as classifier using the training vectors with 12 elements. The experimental results show that the proposed scheme provides a better discriminate performance than previously proposed methods that require a larger amount of feature elements, such as 27, 35 and 225 feature elements, respectively, for their discriminations.
AB - In this paper, we propose a compact image steganalysis method for the LSB-matching steganography, in which a feature vector composed by only 12 elements is extracted from the image. We analyze the statistical artifact occurred in images when a secret data is embedded in it by the LSB-matching steganography. We selected 12 most relevant features based on the probability density function (PDF) of difference of adjacent pixels and the co-occurrence matrix of the image, which can distinguish stegoimages from the natural images. The Support Vector Machine (SVM) is employed as classifier using the training vectors with 12 elements. The experimental results show that the proposed scheme provides a better discriminate performance than previously proposed methods that require a larger amount of feature elements, such as 27, 35 and 225 feature elements, respectively, for their discriminations.
KW - Co-occurrence matrix
KW - LSB Matching steganography
KW - Shape parameter
KW - Steganalysis
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85021805396&partnerID=8YFLogxK
U2 - 10.1109/IWBF.2017.7935103
DO - 10.1109/IWBF.2017.7935103
M3 - Contribución a la conferencia
AN - SCOPUS:85021805396
T3 - Proceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017
BT - Proceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Workshop on Biometrics and Forensics, IWBF 2017
Y2 - 4 April 2017 through 5 April 2017
ER -