Recent Research Reviews Journal
IRO Journals
Volume 4 — Issue 2 — December 2025

Deep Sight: Unveiling Digital Deception

https://doi.org/10.36548/rrrj.2025.2.008
Deepa N.
Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Anna University, Dindigul, India
Edwin Joel P.
Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Anna University, Dindigul, India
Cletus Sylphia P.
Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Anna University, Dindigul, India
Ahalya G.
Artificial Intelligence and Data Science, PSNA College of Engineering and Technology, Anna University, Dindigul, India
PDF
PDF

How to Cite

N., Deepa, Edwin Joel P., Cletus Sylphia P., and Ahalya G. 2026. “Deep Sight: Unveiling Digital Deception”. Recent Research Reviews Journal 4 (2): 325-37. https://doi.org/10.36548/rrrj.2025.2.008.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Deepfakes
— AI Detection
— Media Forensics
— Cybersecurity
— Digital Integrity
Published: 05-02-2026

Abstract

Deepfakes are rapidly increasing and generating severe problems with fake news, fraud and criminal behavior. The issue with AI-generated media has evolved into more accurate and recognizing fake data. The proposed "Deep Sight: Unveiling Digital Deception" is an AI/ML-driven system detects deepfake in various kinds of media including images, videos and audio. It compares differences in facial features, voice structures and action behavior to recognize fake data. This system separates each component (video, audio, images and contents) into real and fake data to improve transparency. It provides subtitle to show the sections of the text to be edited by AI. A built-in monitoring tool allows user to report possible data directly transferred to cybersecurity authorities for immediate action. The "Deep Sight" improves digital responsibility using advanced detection techniques and addressing AI-driven fraud. It enables people and organizations to protect media quality by avoiding modification. This concept proposes to create a safe global network by solving the increasing risk of deepfake technologies.

References

  1. Rossler, Andreas, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. "Faceforensics++: Learning to detect manipulated facial images." In Proceedings of the IEEE/CVF international conference on computer vision, (2019): 1-11.
  2. Dolhansky, Brian, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. "The deepfake detection challenge (dfdc) dataset." arXiv preprint arXiv:2006.07397 (2020).
  3. Korshunov, Pavel, and Sébastien Marcel. "Vulnerability assessment and detection of deepfake videos." In 2019 International Conference on Biometrics (ICB), IEEE, (2019): 1-6.
  4. Ahmed, Naveed Ur Rehman, Afzal Badshah, Hanan Adeel, Ayesha Tajammul, Ali Daud, and Tariq Alsahfi. "Visual deepfake detection: Review of techniques, tools, limitations, and future prospects." IEEE Access 13 (2024): 1923-1961.
  5. Li, Yuezun, Ming-Ching Chang, and Siwei Lyu. "In ictu oculi: Exposing ai created fake videos by detecting eye blinking." In 2018 IEEE International workshop on information forensics and security (WIFS), Ieee, (2018): 1-7.
  6. Güera, David, and Edward J. Delp. "Deepfake video detection using recurrent neural networks." In 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS), IEEE, (2018): 1-6.
  7. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020). DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection. Information Fusion, 64, 131-148.
  8. Kumar, Manoj, Praveen Kumar Rai, and Pankaj Kumar. "A novel approach for detecting deepfake face using machine learning algorithms." In 2024 2nd International Conference on Disruptive Technologies (ICDT), IEEE, (2024): 1588-1592.
  9. Yang, Xin, Yuezun Li, and Siwei Lyu. "Exposing deep fakes using inconsistent head poses." In ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, (2019): 8261-8265.
  10. Mirsky, Yisroel, and Wenke Lee. "The creation and detection of deepfakes: A survey." ACM computing surveys (CSUR) 54, no. 1 (2021): 1-41.
  11. Verdoliva, Luisa. "Media forensics and deepfakes: an overview." IEEE journal of selected topics in signal processing 14, no. 5 (2020): 910-932.
  12. Sabir, Ekraam, Jiaxin Cheng, Ayush Jaiswal, Wael AbdAlmageed, Iacopo Masi, and Prem Natarajan. "Recurrent convolutional strategies for face manipulation detection in videos." Interfaces (GUI) 3, no. 1 (2019): 80-87.
  13. Korshunov, Pavel, and Sébastien Marcel. "Deepfakes: a new threat to face recognition? assessment and detection." arXiv preprint arXiv:1812.08685 (2018).
  14. Agarwal, Shruti, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, and Hao Li. "Protecting world leaders against deep fakes." In CVPR workshops, vol. 1, no. 38. 2019.
  15. https://www.kaggle.com/datasets