Deep Fake Detection System

Abstract

Deepfake technology uses advanced artificial intelligence and deep learning techniques to generate highly realistic synthetic images. While this technology has useful applications in areas such as entertainment and media production, it can also be misused to manipulate digital content and spread misleading information. Because of this, identifying deepfake images has become an important challenge in digital media security. This project presents an AI-based Deepfake Detection System designed to distinguish between real and manipulated images. The proposed approach utilizes a Convolutional Neural Network (CNN) to analyze facial features and extract spatial patterns from images. The model learns to recognize visual inconsistencies such as texture distortions, blending artifacts, and unnatural facial characteristics that often appear in manipulated images. By analyzing these patterns, the system classifies images as real or fake and provides a confidence score for the prediction. The experimental results show that the CNN-based approach can effectively detect deepfake images and improve the reliability of digital media verification.