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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Veröffentlicht in:	Multimedia tools and applications Jg. 84; H. 1; S. 147 - 163
Hauptverfasser:	Agarwal, Saurabh, Walia, Savita, Jung, Ki-Hyun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2025 Springer Nature B.V
Schlagworte:	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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Abstract	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.
AbstractList	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. 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.
Author	Agarwal, Saurabh Jung, Ki-Hyun Walia, Savita
Author_xml	– sequence: 1 givenname: Saurabh surname: Agarwal fullname: Agarwal, Saurabh organization: Department of Software Convergence, Andong National University, Department of Information and Communication Engineering, Yeungnam University – sequence: 2 givenname: Savita surname: Walia fullname: Walia, Savita organization: Chitkara University Institute of Engineering and Technology, Chitkara University – sequence: 3 givenname: Ki-Hyun orcidid: 0000-0002-0662-8355 surname: Jung fullname: Jung, Ki-Hyun email: kingjung@anu.ac.kr organization: Department of Software Convergence, Andong National University
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Snippet	Splicing and copy-paste are popular means of blind digital image manipulation. In this article, a novel identification of composite splicing and copy-paste... Splicing and copy-paste are popular means of blind digital image manipulation. In this article, a novel identification of composite splicing and copy-paste...
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SubjectTerms	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
Title	A cohesive forgery detection for splicing and copy-paste in digital images
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