AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI.
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| Title: | AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI. |
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| Authors: | Kadam, Kalyani1 kalyanik@sitpune.edu.in, Ahirrao, Swati1, Kotecha, Ketan2 |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Oct2021, Vol. 11 Issue 5, p4489-4501. 13p. |
| Subjects: | Adobe Photoshop (Computer software), Deep learning, Forgery, Online social networks, Artificial intelligence, Machine learning, Electronic newspapers, Digital media |
| Abstract: | Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm's validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box's decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 150461914 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kadam%2C+Kalyani%22">Kadam, Kalyani</searchLink><relatesTo>1</relatesTo><i> kalyanik@sitpune.edu.in</i><br /><searchLink fieldCode="AR" term="%22Ahirrao%2C+Swati%22">Ahirrao, Swati</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kotecha%2C+Ketan%22">Kotecha, Ketan</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Oct2021, Vol. 11 Issue 5, p4489-4501. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Adobe+Photoshop+%28Computer+software%29%22">Adobe Photoshop (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forgery%22">Forgery</searchLink><br /><searchLink fieldCode="DE" term="%22Online+social+networks%22">Online social networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+newspapers%22">Electronic newspapers</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+media%22">Digital media</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm's validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box's decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=150461914 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v11i5.pp4489-4501 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 4489 Subjects: – SubjectFull: Adobe Photoshop (Computer software) Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Forgery Type: general – SubjectFull: Online social networks Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Electronic newspapers Type: general – SubjectFull: Digital media Type: general Titles: – TitleFull: AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kadam, Kalyani – PersonEntity: Name: NameFull: Ahirrao, Swati – PersonEntity: Name: NameFull: Kotecha, Ketan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 11 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
| ResultId | 1 |
