An Optimized Low Volume Blind Universal Steganalyzer with improved Generalization
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چکیده
Background: Generic Steganalysis proves to be a boon when there is a suspicion of covert channels with no other information regarding stego images. With the advent of sophisticated steganographic techniques, the process becomes tough as the hidden data is very meager and leaves undecipherable artifacts. A Universal, Blind and Statistical Steganalyzer needs to be more generalized as it encounters unseen stego images created out of any steganographic software working in spatial/transform domain altering any type of feature of the cover image. Also it needs to provide more detection accuracy for the next phase of active steganalysis to proceed successfully. Objective: This paper proposes mixed blind generic classification which attempts to improve the generalization of the classifier. The designed Steganalyzer makes use of hybrid composite / concatenated feature set along with a Sequential Minimal Optimisation (SMO) classifier to set aside stego images from that of cover images. Feature selection based on F-Score has been employed for this work to address the dimensionality problem. Results: Comparison of the obtained results show the efficiency of our approach over SPAM features, the benchmark standard for Steganalysis. Conclusion: Thus a Mixed Blind Universal Steganalyser encompassing a multitude of features with effective feature selection is presented for generalized classification of low volume payloads. Article history: Received X X 201XReceived in revised form X X X 201X Accepted X X 201X
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تاریخ انتشار 2016