Current Volume 9
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.
Deepfake Detection, Convolutional Neural Network, Transformer Model, Facial Forgery, Digital Forensics, Media Authentication, Grad-CAM, FaceForensics++, Deep Learning
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, 9(10) https://doi.org/10.64388/IREV9I10-1717037