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Deepfake video detection system using artificial intelligence

 Department: Computer Science  
 By: usericon olagok01  

 Project ID: 8843
   Rating:  (5.0) votes: 1
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   Price:₦5000
Abstract
The rapid advancement of artificial intelligence, particularly deep learning techniques like Generative Adversarial Networks (GANs), has made the creation of highly realistic synthetic media, known as deepfakes, increasingly accessible. While the technology has legitimate applications, its malicious use for misinformation, identity theft, financial fraud, and political manipulation poses a severe threat to individual privacy, public trust, and national security. The human ability to discern these sophisticated forgeries is becoming increasingly unreliable, necessitating the development of robust, automated detection systems. This project aims to design, develop, and evaluate an AI-powered deepfake detection system to automatically distinguish authentic media from AI-generated manipulations. The proposed solution employs a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction to identify visual artifacts and inconsistencies within individual video frames, combined with Long Short-Term Memory (LSTM) networks to analyze temporal patterns and inconsistencies across frames, such as unnatural facial movements or blinking rates. The system will be trained and validated on comprehensive datasets such as FaceForensics++ and Celeb-DF to ensure robustness and generalization. Performance will be rigorously evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The outcome of this research is a functional prototype that contributes to the field of digital forensics by providing a scalable tool to help social media platforms, news organizations, and cybersecurity agencies combat the pervasive threat of deepfake technology. ...
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