CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature

Bibliographic Details
Title:	CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature
Authors:	Sang In Lee, Jun Young Park, Il Kyu Eom
Source:	IEEE Access, Vol 10, Pp 106217-106229 (2022)
Publisher Information:	IEEE, 2022.
Publication Year:	2022
Collection:	LCC:Electrical engineering. Electronics. Nuclear engineering
Subject Terms:	Copy-move forgery, copy-move forgery localization, convolutional neural network, rotation-invariant, stationary wavelet transform, root-mean squared energy, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
More Details:	This paper introduces a machine learning based copy-move forgery (CMF) localization method. The basic convolutional neural network cannot be applied to CMF detection because CMF frequently involves rotation transformation. Therefore, we propose a rotation-invariant feature based on the root-mean squared energy using high-frequency wavelet coefficients. Instead of using three color image channels, two-scale energy features and low-frequency subband image are fed into the conventional VGG16 network. A correlation module is used by employing small feature patches generated by the VGG16 network to obtain the possible copied and moved patch pairs. The all-to-all similarity score is computed using the correlation module. To generate the final binary localization map, a simplified mask decoder module is introduced, which is composed of two simple bilinear upsampling and two batch-normalized-inception-based mask deconvolution followed by bilinear upsampling. We perform experiments on four test datasets and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	2169-3536
Relation:	https://ieeexplore.ieee.org/document/9911656/; https://doaj.org/toc/2169-3536
DOI:	10.1109/ACCESS.2022.3212069
Access URL:	https://doaj.org/article/27970196d67b4d38a745ca7d946c2ba8
Accession Number:	edsdoj.27970196d67b4d38a745ca7d946c2ba8
Database:	Directory of Open Access Journals

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Items	– Name: Title Label: Title Group: Ti Data: CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sang+In+Lee%22">Sang In Lee</searchLink><br /><searchLink fieldCode="AR" term="%22Jun+Young+Park%22">Jun Young Park</searchLink><br /><searchLink fieldCode="AR" term="%22Il+Kyu+Eom%22">Il Kyu Eom</searchLink> – Name: TitleSource Label: Source Group: Src Data: IEEE Access, Vol 10, Pp 106217-106229 (2022) – Name: Publisher Label: Publisher Information Group: PubInfo Data: IEEE, 2022. – Name: DatePubCY Label: Publication Year Group: Date Data: 2022 – Name: Subset Label: Collection Group: HoldingsInfo Data: LCC:Electrical engineering. Electronics. Nuclear engineering – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Copy-move+forgery%22">Copy-move forgery</searchLink><br /><searchLink fieldCode="DE" term="%22copy-move+forgery+localization%22">copy-move forgery localization</searchLink><br /><searchLink fieldCode="DE" term="%22convolutional+neural+network%22">convolutional neural network</searchLink><br /><searchLink fieldCode="DE" term="%22rotation-invariant%22">rotation-invariant</searchLink><br /><searchLink fieldCode="DE" term="%22stationary+wavelet+transform%22">stationary wavelet transform</searchLink><br /><searchLink fieldCode="DE" term="%22root-mean+squared+energy%22">root-mean squared energy</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+engineering%2E+Electronics%2E+Nuclear+engineering%22">Electrical engineering. Electronics. Nuclear engineering</searchLink><br /><searchLink fieldCode="DE" term="%22TK1-9971%22">TK1-9971</searchLink> – Name: Abstract Label: Description Group: Ab Data: This paper introduces a machine learning based copy-move forgery (CMF) localization method. The basic convolutional neural network cannot be applied to CMF detection because CMF frequently involves rotation transformation. Therefore, we propose a rotation-invariant feature based on the root-mean squared energy using high-frequency wavelet coefficients. Instead of using three color image channels, two-scale energy features and low-frequency subband image are fed into the conventional VGG16 network. A correlation module is used by employing small feature patches generated by the VGG16 network to obtain the possible copied and moved patch pairs. The all-to-all similarity score is computed using the correlation module. To generate the final binary localization map, a simplified mask decoder module is introduced, which is composed of two simple bilinear upsampling and two batch-normalized-inception-based mask deconvolution followed by bilinear upsampling. We perform experiments on four test datasets and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article – Name: Format Label: File Description Group: SrcInfo Data: electronic resource – Name: Language Label: Language Group: Lang Data: English – Name: ISSN Label: ISSN Group: ISSN Data: 2169-3536 – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://ieeexplore.ieee.org/document/9911656/; https://doaj.org/toc/2169-3536 – Name: DOI Label: DOI Group: ID Data: 10.1109/ACCESS.2022.3212069 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/27970196d67b4d38a745ca7d946c2ba8" linkWindow="_blank">https://doaj.org/article/27970196d67b4d38a745ca7d946c2ba8</link> – Name: AN Label: Accession Number Group: ID Data: edsdoj.27970196d67b4d38a745ca7d946c2ba8
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RecordInfo	BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/ACCESS.2022.3212069 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 106217 Subjects: – SubjectFull: Copy-move forgery Type: general – SubjectFull: copy-move forgery localization Type: general – SubjectFull: convolutional neural network Type: general – SubjectFull: rotation-invariant Type: general – SubjectFull: stationary wavelet transform Type: general – SubjectFull: root-mean squared energy Type: general – SubjectFull: Electrical engineering. Electronics. Nuclear engineering Type: general – SubjectFull: TK1-9971 Type: general Titles: – TitleFull: CNN-Based Copy-Move Forgery Detection Using Rotation-Invariant Wavelet Feature Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sang In Lee – PersonEntity: Name: NameFull: Jun Young Park – PersonEntity: Name: NameFull: Il Kyu Eom IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 21693536 Numbering: – Type: volume Value: 10 Titles: – TitleFull: IEEE Access Type: main
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