Abstract: The rapid advancement of deep learning technologies has led to the rise of deepfake media, posing serious threats to digital trust and security. This project presents an AI-based Deepfake Detection System that identifies manipulated images using advanced machine learning techniques. The system integrates a React frontend and a Node.js backend for seamless media upload and preprocessing. The processed content is analysis using deep learning models such as Pixel Error level analysis to detect visual and temporal inconsistencies. Based on the extracted features, the system classifies the media as real or deepfake and provides a confidence score along with an explanatory result. The proposed solution aims to enhance digital content verification and strengthen cybersecurity measures against synthetic media threats.

Keywords: Deepfake Detection, Artificial Intelligence, Pixel Error Level Analysis, Image Forensics, Machine Learning, Cybersecurity, Media Authentication

Downloads: | DOI: 10.17148/IARJSET.2026.13302