Published Paper Details:

DeepFake technology, driven by artificial intelligence, has transformed digital media manipulation, making it possible to create highly realistic synthetic images and videos. While this advancement offers benefits in entertainment and content production, it also poses significant ethical and security challenges, such as misinformation, identity fraud, and unauthorized impersonation. This research focuses on developing an advanced DeepFake detection system that leverages machine learning techniques to identify and counteract these risks. The proposed system comprises two key components: the User Module, which allows user registration, media uploads for detection, and result tracking, and the Admin Module, responsible for content moderation and system oversight. Utilizing sophisticated deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the system is designed to deliver high detection accuracy, real-time processing, and adaptability to evolving DeepFake techniques. This study emphasizes the importance of automated DeepFake detection in maintaining digital authenticity and trust. It addresses the increasing threats posed by manipulated media across various sectors, including journalism, cybersecurity, and social media governance.