Digital Image Processing

Zeinab F. Elsharkawy ¹, Safey A. Abdelwahab ¹, Moawad I. Dessouky ², Sayed M. Elaraby ³, Fathi E. Abd El-Samie ²

Affiliations
1 Engineering Department, Nuclear Research Center, Atomic Energy Authority, Cairo, EG
2 Department of Electronics and Electrical Communications, Menoufia University, Menouf, EG
3 Engineering Department, Nuclear Research Center, Atomic Energy Authority, Cairo, IN

Abstract

Scanner identification is the ability to discern the devices by which an image was scanned. In this paper, a new and robustness individual source scanner identification scheme is proposed. This scheme formulates a unique fingerprint for each scanner using traces of dust, dirt, scratches, and source imperfection pattern over scanner platen on scanned images. A single Support Vector machine (SVM) classifier is implemented and trained using correlation features of scanned images to classify different scanners brands and different models for the same scanner brand, and a 99.79% detection accuracy is obtained. In addition, the robustness of the used individual source scanner identification scheme on resized different resolutions is experimentally tested. The aging effect is also experimentally tested by re-applying the proposed algorithm on the scanned images after a continuous usage of the scanners under test for certain long periods. The experimental results using the proposed classifier for different scanner brands and different models for the same scanner brand approved the validity, efficiency, and robustness of the proposed scheme to match the scanned image to its unique source.

Keywords

Image Classification, Digital Image Forensics, Support Vector Machine.

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Mohammed Y. Abbass ¹, Safey A. Abdelwahab ¹, Salah M. Diab ², Bassiony M. Salam ², El-Sayed M. El-Rabaie ², Fathi E. Abd El-Samie ², Said S. Haggag ³

Affiliations
1 Engineering Department, Nuclear Research Center, Atomic Energy Authority, EG
2 Department of Electronics and Electrical Communications, Menoufia University, Menouf, EG
3 Nuclear Research Center, Atomic Energy Authority, EG

Abstract

This paper deals with the problem of blind separation of digital images from mixtures. A method to solve this problem is blind source separation (BSS) using independent component analysis (ICA). It proposes a wavelet based ICA method using Kurtosis for blind image source separation. In this method, the observations are transformed into an adequate representation using wavelet packets decomposition and Kurtosis criterion. The simulation results of performance measures show a considerable improvement when compared to FastICA. The Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE) and and Segmental Signal-to-Noise Ratio (SNRseg) are used to evaluate the quality of the separated images.

Keywords

Blind Source Separation (BSS), ICA, Kurtosis.

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