Deep Fake Face Detection Using Deep Learning

Abstract

Artificial Intelligence, deepfake technology, Generative Adversarial Networks GAN, Detection System, Detection Accuracy, User accessibility, Digital content verification. Abstract: In recent years, the rise of deepfake technology has raised significant concerns. regarding the authenticity of digital content. Deepfakes, which are synthetic media created using advanced artificial intelligence techniques, can mislead viewers and pose risks to personal privacy, public trust, and social discourse. The proposed system focuses on developing a Generative Adversarial Network (GAN)- based deepfake detection system that aims to identify manipulated images and videos accurately and efficiently. The importance of this research lies in its potential to enhance digital content verification, ultimately restoring trust in media across various sectors, including news, entertainment, and social media. The proposed approach utilizes GANs to both generate synthetic deepfake samples for training and serve as the basis for the detection engine. By focusing solely on GANs, the system leverages their unique capabilities to create a model that is adaptable to evolving deepfake generation techniques. The architecture of the system includes a user-friendly frontend, a robust backend, and a powerful detection engine, all integrated seamlessly to ensure real- time processing and analysis of media files.