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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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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21653
Article Published : 8541
Total Authors : 22459
Total Reviewer : 811
Total Countries : 159
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| Paper Title: | A Survey on Deep Learning Approaches for Deepfake Detection |
| Authors Name: | Suraj Kumar , Mr. Anurag Shrivastava |
| Download E-Certificate: | Download |
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IJRTI_205394
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| Published Paper Id: | IJRTI2507112 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | |
| Abstract: | The proliferation of deepfakes—synthetically generated media using deep learning techniques—poses significant threats to information authenticity, privacy, and public trust. With increasing accessibility to generative models such as Generative Adversarial Networks (GANs) and autoencoders, the manipulation of audio, images, and videos has become more sophisticated and difficult to detect through traditional methods. This survey aims to provide a comprehensive overview of recent deep learning approaches for deepfake detection. It explores state-of-the-art techniques, including CNN-based classifiers, RNNs for temporal analysis, attention mechanisms, and multimodal learning strategies. In addition, it highlights benchmark datasets like FaceForensics++, DFDC, and Celeb-DF, which facilitate model training and evaluation. The paper also discusses key challenges such as generalization across unseen manipulations, adversarial robustness, and computational efficiency. Through comparative analysis, the survey identifies strengths, limitations, and performance trends in existing models, providing valuable insights for researchers and practitioners. This work serves as a foundational reference for developing more resilient and accurate deepfake detection systems in an era where visual misinformation is increasingly prevalent. |
| Keywords: | Deep Learning, Deepfake; Detection; Deep learning; Fake; Video forgery; Image forgery, CNN, LSTM |
| Cite Article: | "A Survey on Deep Learning Approaches for Deepfake Detection ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.b88-b90, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507112.pdf |
| Downloads: | 000377 |
| ISSN: |
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: IJRTI2507112
Registration ID:205394
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier):
Page No: b88-b90 Country: Bhopal, MP, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507112 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507112 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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