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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21604
Article Published : 8531
Total Authors : 22438
Total Reviewer : 805
Total Countries : 159
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Published Paper Details
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| Paper Title: | Image Forgery Detection using Deep Learning: A Review |
| Authors Name: | Khushboo Dewangan , Mohd.Shajid Ansari , Parineeta Jha |
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IJRTI_201281
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| Published Paper Id: | IJRTI2503075 |
| Published In: | Volume 10 Issue 3, March-2025 |
| DOI: | |
| Abstract: | Image forgery, or the manipulation of digital images, poses a serious threat to the authenticity and reliability of visual content distributed through newspapers, magazines, the Internet, and scientific publications. With advanced editing tools like Photoshop, GIMP, and Corel Draw, distinguishing an original image from a manipulated one can be challenging. Traditional detection methods rely on hand-crafted features to identify specific types of tampering, but their effectiveness is often limited. In contrast, deep learning-based approaches have demonstrated higher accuracy by automatically extracting complex patterns from images. This paper provides a comprehensive review of deep learning techniques for image forgery detection, including an analysis of publicly available datasets and an evaluation of various deep learning models such as Convolutional Neural Networks (CNNs). We discuss their applications in detecting different types of forgeries, compare their performance on benchmark datasets, and explore existing challenges and future directions. Our review serves as a valuable resource for researchers and practitioners in digital forensics and image authenticity verification. |
| Keywords: | Image forgery detection, deep learning, digital forensics, image manipulation, convolutional neural networks, Recurrent Neural Networks, Transfer learning. |
| Cite Article: | "Image Forgery Detection using Deep Learning: A Review", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a586-a593, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503075.pdf |
| Downloads: | 000748 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2503075
Registration ID:201281
Published In: Volume 10 Issue 3, March-2025
DOI (Digital Object Identifier):
Page No: a586-a593 Country: Durg, Chhattisgarh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2503075 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2503075 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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