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Volume 6, Issue 1 (9-2009)                   JSDP 2009, 6(1): 53-70 | Back to browse issues page

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JPEG Image Steganalysis Based on Classification of Statistical Features and Two Stage Decision Making. , JSDP. 2009; 6 (1) :53-70
URL: http://jsdp.rcisp.ac.ir/article-1-744-en.html
JPEG Image Steganalysis Based on Classification of Statistical Features and Two Stage Decision Making
Abstract:   (6252 Views)

Abstract In this paper, we propose a comprehensive steganalysis scheme for JPEG images. In this method, the optimized features which can interpret high distinction between cover and stego images are extracted from images. These features have been selected after a careful study on modifications caused by different steganography algorithms on statistical characteristics of images. Furthermore, using a hierarchical decision making, we have considerably improved the detection accuracy. It has been shown that the first-order statistics of DCT coefficients (e.g. histogram) are often more successful than second-order statistics (e.g. different types of correlations) in detection of LSB flipping methods such as JSteg, OutGuess, JPHide&Seek and StegHide. On the other hand, the second order statistical characteristics have better performance in some other steganography methods (especially for LSB Matching, MB1, SSIS and PQ). The proposed Method reveals the weakness of the different steganography algorithms by thorough view on them. The results of our experiments indicate that the accuracy of proposed approach is better than some other state of the art steganalysis methods in the term of detection accuracy. Besides, it is more generalized and comprehensive. A database including 2000 JPEG images with different quality factors has been used for these experiments. The new scheme can detect six common steganography methods: JSteg, OutGuess, F5, MB1, Sequential and Random LSB Matching, with accuracies higher than 80% for the payload of more than 20%. We have used SVM for our classification scheme.
Keywords: Blind JPEG Steganalysis, data hiding attack, LSB flipping, LSB matching, feature categorization
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Type of Study: Research | Subject: Paper
Received: 2009/09/22 | Accepted: 2018/02/19 | Published: 2018/02/19 | ePublished: 2018/02/19
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