Deepfake Detection Using Machine Learning and Computer Vision

Deepfake videos employ artificial intelligence to manipulate a person's facial appearance or voice, potentially spreading misinformation, facilitating identity theft, or causing harassment.This study introduces a robust deepfake detec-tion framework using machine learning and computer vision.Video content is analyzed frame by frame to identify subtle facial irregularities and pixel-level inconsistencies.Convolutional Neural Networks (CNN) capture spatial features, Long Short-Term Memory (LSTM) networks model temporal dynamics, and a hybrid CNN-LSTM model combines both approaches for improved detection accuracy.Experiments on publicly available datasets demonstrate a 92% detection accuracy.The system supports real-time identification of deepfakes, promoting online trust and safer digital interactions.