Live Face Detection

Abstract In the modern digital era, the rapid advancement of image editing and deepfake technologies has made it increasingly difficult to distinguish authentic visual content from manipulated media. The misuse of altered images poses significant risks in areas such as social media, journalism, digital forensics, and identity verification. This project presents a Live Face Detection and Image Authenticity Analysis System developed using Python and Convolutional Neural Networks (CNNs) to address challenges related to digital image manipulation and trustworthiness. The system utilizes computer vision techniques for real-time face detection in video streams and incorporates machine learning methodologies to analyze visual features that help differentiate genuine images from manipulated content. The architecture involves image pre-processing, feature extraction through CNN layers, model training using labeled datasets, and post-processing techniques such as non-maximum suppression to enhance detection accuracy. The proposed solution demonstrates efficient real-time performance and reliable face localization under varying lighting and environmental conditions. By exploring concepts related to image forgery detection, deepfake awareness, and biometric analysis, the project contributes toward strengthening digital media integrity and security.