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Image steganalysis ranges from detecting the presence of covert information in an image (passive steganalysis) to extraction of the information from the stego image (active steganalysis). One of the steps in active steganalysis is determining the stego algorithm used to produce the stego image. In this paper, a low dimensional combination of textural features is adapted for steganalysis. Also a novel blind statistical steganalyser to determine the spatial domain Least Significant Bit (LSB) based algorithms using one against one multi class classification is proposed. The proposed steganalyser is a multi class ensemble Fisher Linear Discriminant (FLD) classifier that uses novel low dimensional textural features for steganalysis. The performed experiments on the Bossbase database for 5 different LSB based algorithms for 8 different payloads show that the results are much better than the state of art steganalyser
Title: test
Description:
Image steganalysis ranges from detecting the presence of covert information in an image (passive steganalysis) to extraction of the information from the stego image (active steganalysis).
One of the steps in active steganalysis is determining the stego algorithm used to produce the stego image.
In this paper, a low dimensional combination of textural features is adapted for steganalysis.
Also a novel blind statistical steganalyser to determine the spatial domain Least Significant Bit (LSB) based algorithms using one against one multi class classification is proposed.
The proposed steganalyser is a multi class ensemble Fisher Linear Discriminant (FLD) classifier that uses novel low dimensional textural features for steganalysis.
The performed experiments on the Bossbase database for 5 different LSB based algorithms for 8 different payloads show that the results are much better than the state of art steganalyser.
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