Published Paper Details:

Deepfake technology, driven by artificial intelligence, has developed rapidly over the past few years, raising issues of misinformation, privacy violations, and online security threats. This project is centered around creating a robust Deepfake Detection System based on machine learning methods to distinguish real media from the manipulated one. The system has a user authentication module for secure access via a login system. In addition, it incorporates an advanced deepfake detection algorithm that can scan images and videos to verify whether they are authentic. The detection model generates a fake accuracy percentage, reflecting how much media are likely manipulated. This measure adds transparency and gives users quantifiable feedback into possible deepfake risks. The system utilizes convolutional neural networks (CNNs) and deep learning to make high-precision identification of synthetic content. The technology can be applied to real-world scenarios such as media authentication, law enforcement, and social media surveillance, helping in the mitigation against misinformation. To make it scalable and efficient, the platform will be developed with an easy-to-use interface where individuals and organizations can upload and examine media easily.Through the creation of a correct and accessible detection system, we are moving closer to maintaining trust in digital content and preventing the risks involved in synthetic media manipulation.