AI-Based Deepfake Detection System: Using Convolutional Neural Networks and Transformer-Based Models

• Author(s): Tirth Gajjar; Yashpalsinh Zala
• Paper ID: 1717037
• Page: 3679-3683
• Published Date: 01-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026
• DOI: https://doi.org/10.64388/IREV9I10-1717037

Abstract

The rapid advancement of generative artificial intelligence has led to the widespread creation and distribution of deepfake media — synthetically generated images, videos, and audio that can convincingly imitate real individuals. Deepfakes pose a critical threat to digital trust, misinformation prevention, and public security. Traditional methods of media authentication are no longer sufficient to detect these highly realistic forgeries. This research proposes an AI-based deepfake detection system that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with a Transformer-based attention mechanism for temporal and contextual analysis. The proposed model is trained on benchmark datasets including FaceForensics++ and DFDC (Deepfake Detection Challenge), enabling it to recognize subtle visual artifacts, inconsistencies in facial geometry, and unnatural blending patterns. Experimental results demonstrate a detection accuracy of 97.3% across diverse manipulation techniques, outperforming existing baseline models significantly. The system also incorporates an explainability module using Grad-CAM visualizations to highlight the regions that contributed to each detection decision. The findings of this study confirm that a hybrid deep learning approach provides a robust and generalizable solution for real-world deepfake detection, contributing meaningfully to the fields of digital forensics, AI ethics, and cybersecurity.

Keywords

Deepfake Detection, Convolutional Neural Network, Transformer Model, Facial Forgery, Digital Forensics, Media Authentication, Grad-CAM, FaceForensics++, Deep Learning

Citations

IRE Journals:
Tirth Gajjar, Yashpalsinh Zala "AI-Based Deepfake Detection System: Using Convolutional Neural Networks and Transformer-Based Models" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3679-3683 https://doi.org/10.64388/IREV9I10-1717037

IEEE:
Tirth Gajjar, Yashpalsinh Zala "AI-Based Deepfake Detection System: Using Convolutional Neural Networks and Transformer-Based Models" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026, doi: https://doi.org/10.64388/IREV9I10-1717037

APA:
Tirth Gajjar, Yashpalsinh Zala (2026). AI-Based Deepfake Detection System: Using Convolutional Neural Networks and Transformer-Based Models. Iconic Research And Engineering Journals, 9(10). doi: https://doi.org/10.64388/IREV9I10-1717037

MLA:
Tirth Gajjar, Yashpalsinh Zala "AI-Based Deepfake Detection System: Using Convolutional Neural Networks and Transformer-Based Models" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026. Crossref, https://doi.org/10.64388/IREV9I10-1717037

BibTeX

@article{1717037,
author = {Tirth Gajjar, Yashpalsinh Zala},
title = {AI-Based Deepfake Detection System: Using Convolutional Neural Networks and Transformer-Based Models},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {3679-3683},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717037.pdf},
abstract = {The rapid advancement of generative artificial intelligence has led to the widespread creation and distribution of deepfake media — synthetically generated images, videos, and audio that can convincingly imitate real individuals. Deepfakes pose a critical threat to digital trust, misinformation prevention, and public security. Traditional methods of media authentication are no longer sufficient to detect these highly realistic forgeries. This research proposes an AI-based deepfake detection system that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with a Transformer-based attention mechanism for temporal and contextual analysis. The proposed model is trained on benchmark datasets including FaceForensics++ and DFDC (Deepfake Detection Challenge), enabling it to recognize subtle visual artifacts, inconsistencies in facial geometry, and unnatural blending patterns. Experimental results demonstrate a detection accuracy of 97.3% across diverse manipulation techniques, outperforming existing baseline models significantly. The system also incorporates an explainability module using Grad-CAM visualizations to highlight the regions that contributed to each detection decision. The findings of this study confirm that a hybrid deep learning approach provides a robust and generalizable solution for real-world deepfake detection, contributing meaningfully to the fields of digital forensics, AI ethics, and cybersecurity.},
keywords = {Deepfake Detection, Convolutional Neural Network, Transformer Model, Facial Forgery, Digital Forensics, Media Authentication, Grad-CAM, FaceForensics++, Deep Learning},
month = {April}
}