Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn

  • Isamadeen A. Khalifa Department of Networks, Bardarash Technical Institute, Duhok Polytechnic University, Kurdistan Region, Iraq (Isamadeen.khalif@dpu.edu.krd)
  • Subhi R.M. Zeebaree Duhok Polytechnic University, Kurdistan Region, Iraq (subhi.rafeeq@dpu.edu.krd) *Tishk University-College of Engineering-Erbil, Kurdistan Region, Iraq (subhi.rafeeq@dpu.edu.krd)
  • Musa Ataş Department of Computer Engineering, Engineering Faculty, Siirt University, Siirt, Turkey (musa.atas@siirt.edu.tr)
  • Farhad M. Khalifa Department of Networks, Bardarash Technical Institute, Duhok Polytechnic University, Kurdistan Region, Iraq (farhad.khalifa@dpu.edu.krd)
Keywords: Steganalysis, Co-Occurrence matrix, DWT, DFT, DCT, BPNN

Abstract

In the last two decades, steganalysis has become a fertile research area to minimize the security risks left behind by Misuse of data concealment in digital computer files. As the propagation of hidden writing increased, the need for the steganalysis emerged and grew to a large extent necessary to deter illicit secret communications. This paper introduces a steganalysis system to detect hidden information in images through using co-occurrence matrix, frequency domain transform, the first three moments, and back propagation neural network (BPNN). Four varieties of the system implemented. Firstly, the co-occurrence matrix calculated for the input image, which suspected to be a carrier of hidden secret information. Second, three levels of discrete wavelet transform (DWT) are applied resulting in 12 subbands. Then, those subbands along with the original image are transformed by discrete Fourier transform (DFT) or discrete cosine transform (DCT) to produce 13 subbands. After that, the first three moments are calculated resulting feature vector with 39 features. Finally, BPNN is used as a classifier to determine whether the image is containing hidden information or not. The system is tested with and without co-occurrence matrix, each of them once using DFT and another time using DCT. The results have shown that using co-occurrence matrix with DFT has the highest performance, which was 81.82% on the Hiding Ratio of 0.5 bit per pixel. This work demonstrates a good effect comparing to previous works.

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Published
2019-03-30
How to Cite
Khalifa, I., Zeebaree, S., Ataş, M., & Khalifa, F. (2019). Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn. Science Journal of University of Zakho, 7(1), 27-32. https://doi.org/10.25271/sjuoz.2019.7.1.574
Section
Science Journal of University of Zakho