Steganalysis of JPEG Image Based on High-Dimensional Feature Regularization
Received date: 2016-07-31
Revised date: 2016-08-19
Online published: 2016-09-30
Steganalysis plays a vital role in information security. To improve recognition accuracy of JPEG stego-image, we establish a model of scatter matrix eigenspectrum, and purpose a new method to transfer training features of cover and stego images. The eigenspectrum is first modeled by its distribution and divided into three non-overlapping partitions: areas in which the eigenvalue drops rapidly, areas with stable eigenvalues, and areas of zero eigenvalue. The eigenvector is whitened and regularized in the three partitions with adaptive regularization. The result of these steps is a transfer matrix that transfers the input features. The features are now processed and extracted by picking the first t eigenvectors. The processed feature data are then trained with a Fisher linear discriminant (FLD) ensemble classifier. The result shows that recognition accuracy of JPEG stegoimages with the FLD ensemble is improved after the JPEG stego-image features are sorted, regularized and extracted.
SUN Wu-yi, FENG Guo-rui . Steganalysis of JPEG Image Based on High-Dimensional Feature Regularization[J]. Journal of Applied Sciences, 2016 , 34(5) : 555 -563 . DOI: 10.3969/j.issn.0255-8297.2016.05.008
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