A cohesive forgery detection for splicing and copy-paste in digital images

Splicing and copy-paste are popular means of blind digital image manipulation. In this article, a novel identification of composite splicing and copy-paste manipulation is achieved concurrently on the forgery detection standard datasets Extended IMD2020, CASIA v1.0, and CASIA v2.0. An image under su...

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Vydáno v:Multimedia tools and applications Ročník 84; číslo 1; s. 147 - 163
Hlavní autoři: Agarwal, Saurabh, Walia, Savita, Jung, Ki-Hyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.01.2025
Springer Nature B.V
Témata:
Algorithms
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Digital imaging
Discrete cosine transform
Forgery
Image filters
Image manipulation
Multimedia Information Systems
Special Purpose and Application-Based Systems
Support vector machines
Bicoherence features
Image forensics
AKAZE
Splicing forgery
Copy-paste forgery
ISSN:1573-7721, 1380-7501, 1573-7721
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Shrnutí:Splicing and copy-paste are popular means of blind digital image manipulation. In this article, a novel identification of composite splicing and copy-paste manipulation is achieved concurrently on the forgery detection standard datasets Extended IMD2020, CASIA v1.0, and CASIA v2.0. An image under supervision is taken first, and texture-based Orientation Invariant Local Binary Pattern (OILBP) features are extricated using the Discrete Cosine Transform. The proposed technique uses an SVM classifier to decide whether the input image is spliced. Also, the proposed algorithm can check for copy-paste forgery in the image when not spliced. For copy-paste detection, Accelerated-KAZE (AKAZE) features are used to locate the replicated regions in the image. There is a copy-move forgery in the image to be discovered when the features match after post-processing filtering. Otherwise, the image is authentic. Experimental results illustrate that the performance of the proposed approach is improved than previous works. One of the significant advantages is that two types of forgeries can be detected simultaneously using the proposed cohesive approach.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-18154-7