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New and efficient blind detection algorithm for digital image forgery using homomorphic image processing

  • Zeinab F. ElsharkawyEmail author
  • Safey A. S. Abdelwahab
  • Fathi E. Abd El-Samie
  • Moawad Dessouky
  • Sayed Elaraby
Article
  • 51 Downloads

Abstract

Digital image forgery detection is an important task in digital life as the image may be easily manipulated. This paper presents a novel blind tampering detection algorithm for images acquired from digital cameras and scanners. The algorithm is based on applying homomorphic image processing on each suspicious image to separate illumination from reflectance components. In natural images, it is known that the illumination component is approximately constant, while changes can be detected in tampered ones. Support Vector Machine (SVM) and Neural Network (NN) classifiers are used for classification of tampered images based on the illumination component, and their results are compared to obtain the best classifier performance. The Receiver Operating Characteristic (ROC) curve is used to depict the classifier performance. Three different color coordinate systems are tested with the proposed algorithm, and their results are compared to obtain the highest accuracy level. Joint Photographic Experts Group (JPEG) compressed images with different Quality Factors (QFs) are also tested with the proposed algorithm, and the performance of the proposed algorithm in the presence of noise is studied. The performance of the SVM classifier is better than that of the NN classifier as it is more accurate and faster. A 96.93% detection accuracy has been obtained regardless of the acquisition device.

Keywords

Digital forensics Forgery detection Homomorphic image processing SVM and NN classifiers Color coordinate systems JPEG compression 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Engineering Department, Nuclear Research CenterAtomic Energy AuthorityCairoEgypt
  2. 2.Department of Electronics and Electrical Communications, Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt

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