Fathi Abd El-Samie

Work place: Faculty of Electronic Engineering, Menoufia University,Menouf, Egypt.

E-mail: fathi_sayed@yahoo.com


Research Interests: Image Manipulation, Image Processing, Data Mining


Fathi E. Abd El-Samie received the B.Sc.(Hons.), M.Sc., and Ph.D.degrees from Menoufia University, Menouf, Egypt, in 1998, 2001, and2005 respectively. Since 2005, he has been a Teaching Staff Member with the Department of Electronics and Electrical Communications,Faculty of Electronic Engineering, Menoufia University. He is currently a researcher at KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICs). He is a coauthor of about 200 papers in international conference proceedings and journals, and five textbooks.His current research interests include image enhancement, imagerestoration, image interpolation, super-resolution reconstruction of images, data hiding, multimedia communications, medical image processing, optical signal processing, and digital communications. Dr.Abd ElSamie was a recipient of the Most Cited Paper Award from the Digital Signal Processing journal in 2008.

Author Articles
A Discriminative Statistical Model for Digital Image Forgery Detection

By Amira Baumy Naglaa. F Soiliman Mahmoud Abdalla Fathi Abd El-Samie

DOI: https://doi.org/10.5815/ijem.2016.06.01, Pub. Date: 8 Nov. 2016

The headway of modern technology and facility to use processing software leads to tamper and implicate of digital images. This tampering is being performed without leaving any a clear effect noted with the naked eye. The discrimination between different authentic and forged images can be based on its Probability Density Functions (PDFs). This paper introduces a new model for digital image forgery detection. This framework has two main phases; training and testing. In the training phase, the peak is calculated for the derivatives histogram of the illumination components by using homomorphic filter to separate the illumination components on each image. Firstly, the derivative of illumination histogram for authentic and forged images is calculated then the PDFs are estimated for authentic and forged images, finally the threshold is determined. In the testing phase, the determined threshold is tested with realistic dataset followed by using the selected bins for feature calculation in the prediction process. In the final prediction step, a detection and decision process is performed to obtain performance of the new model. This new model is provided a very effective performance. Different color image contrast systems RGB and HIS are studied and utilized for testing our model and compare between each channel for two systems to estimate performance and obtain more sensitive channel.

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