Abstract | ||
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The threat posed by hackers, spies, terrorists, and criminals, etc. using steganography for stealthy communications and other illegal purposes is a serious concern of cyber security. Several steganographic systems that have been developed and made readily available utilize JPEG images as carriers. Due to the popularity of JPEG images on the Internet, effective steganalysis techniques are called for to counter the threat of JPEG steganography. In this article, we propose a new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images. First, neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array and the absolute array, respectively; then a support vector machine (SVM) is applied to the features for detection. An evolving neural-fuzzy inference system is employed to predict the hiding amount in JPEG steganograms. We also adopt a feature selection method of support vector machine recursive feature elimination to reduce the number of features. Experimental results show that, in detecting several JPEG-based steganographic systems, our method prominently outperforms the well-known Markov-process based approach. |
Year | DOI | Venue |
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2011 | 10.1145/1899412.1899420 | ACM TIST |
Keywords | Field | DocType |
support vector machine,nuero-fuzzy,jpeg,joint density feature,feature selection method,svmrfe,svm,feature elimination,steganalysis,jpeg steganograms,classification,feature mining,neighboring joint density,jpeg steganography,joint density-based jpeg steganalysis,support vector machine recursive,dct coefficient array,steganography,jpeg image,feature selection,cyber security,discrete cosine transform,markov process,machine learning | Data mining,Steganography,Lossless JPEG,Pattern recognition,Feature selection,Computer science,Support vector machine,Discrete cosine transform,JPEG,Artificial intelligence,Quantization (image processing),Steganalysis | Journal |
Volume | Issue | ISSN |
2 | 2 | 2157-6904 |
Citations | PageRank | References |
31 | 1.26 | 40 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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Qingzhong Liu | 1 | 588 | 44.77 |
Andrew H. Sung | 2 | 1034 | 84.10 |
Mengyu Qiao | 3 | 263 | 17.16 |