Detection of tampered real time videos using deep neural networks.
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| Title: | Detection of tampered real time videos using deep neural networks. |
|---|---|
| Authors: | Koshy, Litty1 (AUTHOR) littykoshy@scmsgroup.org, Shyry, S. Prayla1 (AUTHOR) praylashyry.cse@sathyabama.ac.in |
| Source: | Neural Computing & Applications. Apr2025, Vol. 37 Issue 11, p7691-7703. 13p. |
| Subjects: | Artificial neural networks, Editing software, Video editing, Digital technology, Motivation (Psychology), Deep learning, Digital communications |
| Abstract: | In recent years, there has been a significant increase in the creation and sharing of videos that promote the utilization of digitally interactive multimedia, including music, graphics, and videos, across various devices. This trend encompasses both social networking applications and everyday tasks, mirroring the growing reliance on digital communication devices. Forgery techniques and motivations in the digital realm have undergone significant advancements. Previously, video editing methods were employed to enhance digital content. However, the proliferation of affordable and user-friendly video editing software has introduced several drawbacks and risks associated with these editing techniques. These editing tools can be misused to create misleading, altered, or fabricated videos for malicious purposes, such as spreading misinformation, deception, or defamation. In order to produce altered or fraudulent videos, additional footage is mixed, edited, or synthesized. Sophisticated editing techniques can make it challenging to detect forged videos, making it easier for forgeries to be mistakenly perceived as genuine. Existing method uses methods that detect forgery in videos with simply static backgrounds only. Proposed systems uses a deep learning strategy that incorporates transfer learning utilizing VGG16 and Customized CNN layers to categorize real time videos as tampered or authentic. With the aid of deep neural networks, the suggested method may identify forgery in films with both static and moving backgrounds. The experimental findings show that the suggested strategy is more accurate and effective than existing methods also it provides trustworthy results with low computing cost and strong detection performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature 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: 183971797 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Detection of tampered real time videos using deep neural networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Koshy%2C+Litty%22">Koshy, Litty</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> littykoshy@scmsgroup.org</i><br /><searchLink fieldCode="AR" term="%22Shyry%2C+S%2E+Prayla%22">Shyry, S. Prayla</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> praylashyry.cse@sathyabama.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Apr2025, Vol. 37 Issue 11, p7691-7703. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Editing+software%22">Editing software</searchLink><br /><searchLink fieldCode="DE" term="%22Video+editing%22">Video editing</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br /><searchLink fieldCode="DE" term="%22Motivation+%28Psychology%29%22">Motivation (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+communications%22">Digital communications</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In recent years, there has been a significant increase in the creation and sharing of videos that promote the utilization of digitally interactive multimedia, including music, graphics, and videos, across various devices. This trend encompasses both social networking applications and everyday tasks, mirroring the growing reliance on digital communication devices. Forgery techniques and motivations in the digital realm have undergone significant advancements. Previously, video editing methods were employed to enhance digital content. However, the proliferation of affordable and user-friendly video editing software has introduced several drawbacks and risks associated with these editing techniques. These editing tools can be misused to create misleading, altered, or fabricated videos for malicious purposes, such as spreading misinformation, deception, or defamation. In order to produce altered or fraudulent videos, additional footage is mixed, edited, or synthesized. Sophisticated editing techniques can make it challenging to detect forged videos, making it easier for forgeries to be mistakenly perceived as genuine. Existing method uses methods that detect forgery in videos with simply static backgrounds only. Proposed systems uses a deep learning strategy that incorporates transfer learning utilizing VGG16 and Customized CNN layers to categorize real time videos as tampered or authentic. With the aid of deep neural networks, the suggested method may identify forgery in films with both static and moving backgrounds. The experimental findings show that the suggested strategy is more accurate and effective than existing methods also it provides trustworthy results with low computing cost and strong detection performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature 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: 10.1007/s00521-024-09988-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 7691 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Editing software Type: general – SubjectFull: Video editing Type: general – SubjectFull: Digital technology Type: general – SubjectFull: Motivation (Psychology) Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Digital communications Type: general Titles: – TitleFull: Detection of tampered real time videos using deep neural networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Koshy, Litty – PersonEntity: Name: NameFull: Shyry, S. Prayla IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 11 Titles: – TitleFull: Neural Computing & Applications Type: main |
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