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  1/2022 - 7

Image Forgery Detection Using Noise and Edge Weighted Local Texture Features

ASGHAR, K. See more information about ASGHAR, K. on SCOPUS See more information about ASGHAR, K. on IEEExplore See more information about ASGHAR, K. on Web of Science, SADDIQUE, M. See more information about  SADDIQUE, M. on SCOPUS See more information about  SADDIQUE, M. on SCOPUS See more information about SADDIQUE, M. on Web of Science, HUSSAIN, M. See more information about  HUSSAIN, M. on SCOPUS See more information about  HUSSAIN, M. on SCOPUS See more information about HUSSAIN, M. on Web of Science, BEBIS, G. See more information about  BEBIS, G. on SCOPUS See more information about  BEBIS, G. on SCOPUS See more information about BEBIS, G. on Web of Science, HABIB, Z. See more information about HABIB, Z. on SCOPUS See more information about HABIB, Z. on SCOPUS See more information about HABIB, Z. on Web of Science
 
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Download PDF pdficon (1,652 KB) | Citation | Downloads: 1,158 | Views: 1,541

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
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Full text preview
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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 92,491 TCR
SCOPUS® Citations for all references: 154,352 TCR

Web of Science® Average Citations per reference: 1,285 ACR
SCOPUS® Average Citations per reference: 2,144 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-07-10 18:30 in 446 seconds.




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