Naglaa. F Soiliman

Work place: Faculty of Engineering, Zagazig University, Zagazig, Egypt.



Research Interests: Image Processing, Image Manipulation


Naglaa F. Soliman received the B.Sc., M.Sc., and Ph.D. degrees from the faculty of Engineering, Zagazig University, Egypt in 1999, 2004,and 2011, respectively. She was worked as a lecturer at the Department of Electronics and Communications Engineering, Faculty of Engineering,Zagazig University from 2012 up to 2015. She is currently at Faculty of Computer and Inforation Sciences, pricess Nourah Bint Abdulrahman University, Ray . Her areas of interest are digital communications,signal processing, image processing, and coding.

Author Articles
A Discriminative Statistical Model for Digital Image Forgery Detection

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

DOI:, 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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