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- W2034067936 abstract "In this paper, we present a new calibration technique aimed at blind steganalysis for JPEG images, which can magnify the difference between cover images and stego images. The calibration can be considered as a preprocessing of stego image before extracting the features. So the calibrated features are calculated as the difference between a specific function calculated from the original stego image and the same function obtained from calibrated version. Moreover, the calibrated feature was used to train SVM (support vector machine), a nonlinear classifier, which is effective in class separation. For comparison, a database composed of 6690 cover and stego images (generated by using four different embedding schemes) was established. Based on this database, we conducted extensive experiments and drawn a conclusion that the steganalysis based on our novel calibration can detect the stego images with high accuracy." @default.
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- W2034067936 date "2014-06-01" @default.
- W2034067936 modified "2023-09-24" @default.
- W2034067936 title "A Novel Calibration Used for Blind Steganalysis of JPEG Images" @default.
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- W2034067936 doi "https://doi.org/10.4028/www.scientific.net/amr.971-973.1504" @default.
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