IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

DEEP FAKE VIDEO DETECTION USING CNN-LSTM MODEL FOR SPATIAL AND TEMPORRAL FEATURE EXTRACTION

SEDHUSANJAY S SENTHIL C SATHISH KUMAR S

Bachelor of Engineering in Computer Science and Enigineering

The Kavery Engineering College (An Autonomous Institution, Affiliated to Anna University Chennai and Approved By Aicte, New Delhi)
DOI:https://doi.org/10.55041/ijcope.v2i4.591

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Download PDF Review Report

Abstract

In recent years, the emergence of deepfake technology has raised significant concerns regarding its potential to manipulate digital media and deceive audiences worldwide. Deepfakes, which utilize advanced machine learning algorithms to create highly realistic synthetic media, pose a serious threat to the integrity of visual and audio content on the internet. These maliciously altered videos, images, and audio recordings can be used to spread disinformation, impersonate individuals, and undermine trust in online information sources. The rapid advancement of deepfake technology, coupled with the widespread availability of powerful computing resources and open-source software tools, has made it increasingly accessible to individuals with malicious intentions. As a result, there has been a surge in the creation and dissemination of deepfake content across various online platforms, including social media, news websites, and messaging apps.

How to Cite this Paper

S, S., C, S. & S, S. K. (2026). Deep Fake Video Detection Using Cnn-Lstm Model for Spatial and Temporral Feature Extraction. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.591

S, SEDHUSANJAY, et al.. "Deep Fake Video Detection Using Cnn-Lstm Model for Spatial and Temporral Feature Extraction." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.591.

S, SEDHUSANJAY,SENTHIL C, and SATHISH S. "Deep Fake Video Detection Using Cnn-Lstm Model for Spatial and Temporral Feature Extraction." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.591.

Search & Index

Google Scholar Crossref DOI ResearchGate

References


  1.      Zhang, Y., Li, Y., Wang, J., & Li, Y. (2020). Deep Learning for Deepfakes Detection: A Comprehensive Survey. IEEE Transactions on Multimedia.

  2. Cholleti, S. R., & Reddy, V. U. (2021). DeepFake Detection: A Survey. IEEE Access.

  3. Menon, A. K., Balasubramanian, V. N., & Jain, R. (2020). A Survey on Deep Learning Techniques for Video-based Deepfake Detection. Pattern Recognition Letters.

  4. Gupta, A., & Jaiswal, S. (2021). DeepFake Detection Techniques: A Survey. International Journal of Advanced Computer Science and Applications.

  5. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.

  6. Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2019). Faceforensics++: Learning to detect manipulated facial images. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1-1)

  7. Li, Y., Chang, M. C., & Lyu, S. (2018). In ictu oculi: Exposing AI created fake videos by detecting eye blinking. arXiv preprint arXiv:1806.02877.

  8. Marra, F., Gragnaniello, D., & Verdoliva, L. (2020). Silent faces: Realistic 3d facial manipulations in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6579-6588).

  9. Nguyen, A. T., & Yeung, S. (2019). Detecting Deepfake Videos with Temporal Coherence. arXiv preprint arXiv:1911.00686.

  10. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., & Morales, A. (2020). DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection. Information Fusion.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Apr 26 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Back to Volume 02, Issue 04View All Issues Next Article

← Previous Article

Deep Fake Audio Detection Using Deep Learning

Next Article →

Deep Learning for Facial Recognition and Detection
Scroll to Top