Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn
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 d...
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| Format: | Article |
| Language: | English |
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University of Zakho
2019-03-01
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| Series: | Science Journal of University of Zakho |
| Subjects: | |
| Online Access: | https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/574 |
| _version_ | 1811249568398966784 |
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| author | Isamadeen A. Khalifa Subhi R.M. Zeebaree Musa Ataş Farhad M. Khalifa |
| author_facet | Isamadeen A. Khalifa Subhi R.M. Zeebaree Musa Ataş Farhad M. Khalifa |
| author_sort | Isamadeen A. Khalifa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T15:48:58Z |
| format | Article |
| id | doaj.art-b45b5197b61d40bb952f57fe7716fdf2 |
| institution | Directory Open Access Journal |
| issn | 2663-628X 2663-6298 |
| language | English |
| last_indexed | 2024-04-12T15:48:58Z |
| publishDate | 2019-03-01 |
| publisher | University of Zakho |
| record_format | Article |
| series | Science Journal of University of Zakho |
| spelling | doaj.art-b45b5197b61d40bb952f57fe7716fdf22022-12-22T03:26:35ZengUniversity of ZakhoScience Journal of University of Zakho2663-628X2663-62982019-03-0171273210.25271/sjuoz.2019.7.1.574574Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and BpnnIsamadeen A. Khalifa0Subhi R.M. Zeebaree1Musa Ataş2Farhad M. Khalifa3Department of Networks, Bardarash Technical Institute, Duhok Polytechnic University, Kurdistan Region, Iraq (Isamadeen.khalif@dpu.edu.krd)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)Department of Computer Engineering, Engineering Faculty, Siirt University, Siirt, Turkey (musa.atas@siirt.edu.tr)Department of Networks, Bardarash Technical Institute, Duhok Polytechnic University, Kurdistan Region, Iraq (farhad.khalifa@dpu.edu.krd)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.https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/574SteganalysisCo-Occurrence matrixDWTDFTDCTBPNN |
| spellingShingle | Isamadeen A. Khalifa Subhi R.M. Zeebaree Musa Ataş Farhad M. Khalifa Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn Science Journal of University of Zakho Steganalysis Co-Occurrence matrix DWT DFT DCT BPNN |
| title | Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn |
| title_full | Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn |
| title_fullStr | Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn |
| title_full_unstemmed | Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn |
| title_short | Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn |
| title_sort | image steganalysis in frequency domain using co occurrence matrix and bpnn |
| topic | Steganalysis Co-Occurrence matrix DWT DFT DCT BPNN |
| url | https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/574 |
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