Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique.
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| Title: | Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique. |
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| Authors: | Vaishnavi, D.1 (AUTHOR) vaishume11@gmail.com, Balaji, G.N.2 (AUTHOR) |
| Source: | Journal of Intelligent & Fuzzy Systems. 2023, Vol. 45 Issue 6, p10267-10280. 14p. |
| Subjects: | Forgery, Editing software, Artificial intelligence, Video editing, Social networks |
| Abstract: | Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 174544469 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vaishnavi%2C+D%2E%22">Vaishnavi, D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vaishume11@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Balaji%2C+G%2EN%2E%22">Balaji, G.N.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>. 2023, Vol. 45 Issue 6, p10267-10280. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Forgery%22">Forgery</searchLink><br /><searchLink fieldCode="DE" term="%22Editing+software%22">Editing software</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Video+editing%22">Video editing</searchLink><br /><searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/JIFS-230291 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 10267 Subjects: – SubjectFull: Forgery Type: general – SubjectFull: Editing software Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Video editing Type: general – SubjectFull: Social networks Type: general Titles: – TitleFull: Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vaishnavi, D. – PersonEntity: Name: NameFull: Balaji, G.N. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 10641246 Numbering: – Type: volume Value: 45 – Type: issue Value: 6 Titles: – TitleFull: Journal of Intelligent & Fuzzy Systems Type: main |
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