Unmasking Deepfakes: CNNs and Vision Transformers for Cutting-Edge Detection

Deepfake technology, driven by Generative Adversarial Networks (GANs), poses challenges in digital security by enabling highly realistic synthetic media. This study proposes a detection framework combining Convolutional Neural Networks (CNNs) for local feature extraction and Vision Transformers (ViTs) for global analysis. Evaluated on two datasets, CNNs achieved 97.1 % accuracy on a140K-image dataset, outperforming ViTs at 90.06%, though ViTs showed better generalization. Despite these advances, deepfake detection faces challenges like adversarial attacks and dataset biases. Future work will enhance real-time processing, robustness, and multi-modal approaches integratingaudio and behavioral cues.