AI DEEPFAKE DETECTION

Abstract

Deepfakes are artificially generated or manipulated images, videos, or audio created using advanced deep learning techniques. While these technologies can be used for entertainment and creative applications, they also pose serious threats such as misinformation, identity theft, and cybercrime. This project focuses on developing an AI-based deepfake detection system that can automatically identify manipulated media content. The system uses machine learning and deep learning techniques to analyze visual patterns, facial features, and inconsisten- -cies in videos or images. A dataset containing real and fake media samples is used to train the detection model. Image preprocessing, feature extraction, and classification algorithms are applied to improve detection accuracy. The model evaluates media files and predicts whether they are authentic or manipulated. Experimental results demonstrate that AI-based approaches can effectively detect deepfake content with high accuracy. This system can help social media platforms, news organizations, and cybersecurity agencies reduce the spread of fake digital content. Future improvements may include using advanced deep learning models and real-time detection systems to enhance reliability and performance. Deepfake detection has become an important area of research due to the rapid growth of artificial intelligence technologies used to create highly realistic fake media. Deepfake content can manipulate facial expressions, voice, and actions, making it difficult for humans to distinguish between real and fake information. This project aims to design an intelligent system that can detect deepfake images and videos using deep learning techniques such as Convolutional Neural Networks (CNN). The system analyzes facial landmarks, texture patterns, and temporal inconsistencies present in manipulated media. By training the model on a dataset containing both real and fake samples, the system learns to recognize hidden patterns that indicate forgery. The proposed solution can assist in preventing the spread of misinformation, protecting digital identity, and improving online security. The results show that AI-based detection systems can play a vital role in maintaining the authenticity and trustworthiness of digital media.