An Intelligent System for Analysing and Detecting Deepfake Videos: A Deep Learning Approach

The swift rise of Artificial Intelligence (AI) has brought about remarkable technological progress in numerous fields such as media, entertainment, and communication. Among the various outcomes of this advancement, deepfake technology stands out as a contentious issue; it involves using machine learning to artificially change video content. Although deepfakes have potential applications in creativity and education, they also pose significant ethical, legal, and social risks, such as spreading false information, impersonating others, and harming reputations. This increasing danger has underscored the urgency for effective and smart deepfake detection systems that can accurately and swiftly identify altered content. Despite ongoing research, many current deepfake detection models struggle with poor generalization and performance issues when faced with complex data sets. These limitations highlight a notable gap in research concerning the creation of resilient, flexible, and multimodal detection systems that can pinpoint inconsistencies in deepfake videos. This research aims to establish an intelligent model for both detecting and analyzing deepfake videos by utilizing cutting- edge deep learning methods. The study's primary goals are: (i) to create a deep learning framework that uses Long Short-Term Memory (LSTM) with attention mechanisms for analyzing temporal features, while also merging various features through Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) for feature extraction, (ii) to implement a software prototype in Python that can identify videos as either fake or genuine, and (iii) to assess and contrast the effectiveness of existing deepfake detection models with the new system. The research methodology employs agile and responsive software development strategies to facilitate adaptability and ongoing enhancement. Training, testing, and evaluation of the model occur on Google Colab, which allows for GPU acceleration to expedite processing. The dataset comprises multiple types of deepfake and genuine videos, which undergo thorough pre-processing, feature extraction, and fusion before classification. Various performance metrics, including accuracy, precision, recall, and F1-score, are used to assess the model's effectiveness. The main discoveries from this research indicate that the proposed intelligent model significantly boosts detection accuracy compared to current models. By incorporating attention mechanisms and multimodal fusion, the model can identify subtle discrepancies in both video frames and audio signals, thus improving its reliability and durability. The software developed achieved high classification accuracy, proving its applicability in real-life situations. In summary, we have successfully created a sophisticated system for detecting deepfakes that integrates deep learning techniques with contemporary programming resources.