Document Type


Date of Degree Completion

Spring 2020

Degree Name

Master of Science (MS)


Computational Science

Committee Chair

Razvan Andonie

Second Committee Member

Szilard Vajda

Third Committee Member

Boris Kovalerchuk


The issue of forged images is currently a global issue that spreads mainly via social networks. Image forgery has weakened Internet users’ confidence in digital images. In recent years, extensive research has been devoted to the development of new techniques to combat various image forgery attacks. Detecting fake images prevents counterfeit photos from being used to deceive or cause harm to others. In this thesis, we propose methods using the error level analysis algorithm to detect manipulated images. We show that our combination of image pre-processing and machine learning techniques is an efficient approach to detecting image forgery attacks.