Article citationsMore>>
Song, X., Li, Z., Chen, L. and Liu, J. (2016) Entropy Feature Based on 2D Gabor Wavelets for JPEG Steganalysis. In: Wang, G., Ray, I., Alcaraz Calero, J. and Thampi, S., Eds., Security, Privacy and Anonymity in Computation, Communication and Storage. SpaCCS 2016. Lecture Notes in Computer Science, Vol. 10067, Springer, Cham, 59-72.
https://doi.org/10.1007/978-3-319-49145-5_7
has been cited by the following article:
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TITLE:
A Survey on Different Feature Extraction and Classification Techniques Used in Image Steganalysis
AUTHORS:
John Babu, Sridevi Rangu, Pradyusha Manogna
KEYWORDS:
Steganalysis, Steganography, Feature Extraction, Classification
JOURNAL NAME:
Journal of Information Security,
Vol.8 No.3,
July
14,
2017
ABSTRACT: Steganography is the process of hiding data into public digital medium for secret
communication. The image in which the secret data is hidden is termed
as stego image. The detection of hidden embedded data in the image is the
foundation for blind image steganalysis. The appropriate selection of cover
file type and composition contribute to the successful embedding. A large
number of steganalysis techniques are available for the detection of steganography
in the image. The performance of the steganalysis technique depends
on the ability to extract the discriminative features for the identification of
statistical changes in the image due to the embedded data. The issue encountered
in the blind image steganography is the non-availability of knowledge
about the applied steganography techniques in the images. This paper surveys
various steganalysis methods, different filtering based preprocessing methods,
feature extraction methods, and machine learning based classification methods,
for the proper identification of steganography in the image.