Deepfake Detection Using Deep Learning and Convolutional Neural Networks

The rapid development of deepfake technologies has raised serious concerns in fields such as cybersecurity, media, and law enforcement.Detecting deepfake videos accurately is critical to countering the threats posed by synthetic media.This study explores the use of machine learning techniques for deepfake detection.We leverage the FaceForensics++ dataset, which consists of both real and fake videos, and employ deep learning models, including MobileNetV2, to classify videos as real or fake.Our experiments show that the MobileNetV2 model achieves an accuracy of 96% in classifying deepfake videos, outperforming traditional models in terms of both efficiency and accuracy.This paper discusses the methodology, experimental results, and future directions for improving deepfake detection.