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Image Forgery Detection Using Noise and Edge Weighted Local Texture FeaturesASGHAR, K.   , SADDIQUE, M. , HUSSAIN, M. , BEBIS, G. , HABIB, Z. |
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Author keywords
artificial intelligence, forgery, Fourier transforms, machine learning, pattern recognition
References keywords
image(54), detection(35), forgery(28), noise(20), forensics(18), information(15), features(14), security(13), processing(13), multimedia(12)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2022-02-28
Volume 22, Issue 1, Year 2022, On page(s): 57 - 69
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.01007
Web of Science Accession Number: 000762769600006
SCOPUS ID: 85126766756
Abstract
Image forgery detection is important for sensitive domains such as courts of law. The main challenge is to develop a robust model that is sensitive to tampering traces. Existing techniques perform well on a limited dataset but do not generalize well across the datasets. Moreover, these techniques cannot reliably detect tampering that distorts the texture pattern of the image. The noise patterns remain consistent throughout a digital image if its contents are not altered. Based on this hypothesis, a robust descriptor FFT-DRLBP (Fast Fourier Transformation - Discriminative Robust Local Binary Patterns) is introduced, which first estimates noise patterns using FFT and encodes the discrepancies in noise patterns using DRLBP. Features extracted are passed to Support Vector Machine (SVM) for deciding whether the image is authentic or tampered. Intensive experiments are performed on benchmark datasets to validate the performance of the method. It achieved an accuracy of 99.21% on the combination of two challenging datasets. The comparison shows that it outperforms state-of-the-art methods and is vigorous to image forgery attacks even in the presence of various post-processing operations. The performance of the method is also validated using cross-dataset experiments, which ensures its robustness and generalization. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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