International Journal of Computer Techniques Volume 12 Issue 3 | Detecting Deepfakes Using Convolutional Neural Networks
Detecting Deepfakes Using Convolutional Neural Networks
International Journal of Computer Techniques – Volume 12 Issue 3, May – June 2025
Abstract
Deepfake technology leverages **Generative Adversarial Networks (GANs)** to create **synthetic media**, making detection a critical challenge in **digital forensics**. This study presents a **CNN-based detection model**, trained on publicly available datasets, capable of identifying **spatial inconsistencies and subtle artifacts** present in deepfake-generated content.
Keywords
Deepfake Detection, CNNs, Generative Adversarial Networks, Artificial Intelligence, Image Forensics, FaceForensics++, DFDC Dataset, Deep Learning.
Conclusion
The **CNN model developed for deepfake detection** exhibited **strong performance**, successfully identifying **synthetic artifacts left by generative networks**. Future improvements include **enhanced image quality sensitivity, temporal context integration, and real-time deployment**.
References
- Afchar et al. (2018). “MesoNet: a Compact Facial Video Forgery Detection Network.”
- Rossler et al. (2019). “FaceForensics++: Learning to Detect Manipulated Facial Images.”
- Dolhansky et al. (2020). “The Deepfake Detection Challenge Dataset.” arXiv.
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