Deepfake Detection System
This study proposes a novel deepfake detection system that integrates a hybrid Recurrent Neural Network, Convolutional Neural Network, and Long Short-Term Memory architecture, employing a multi-faceted approach to training by utilizing several diverse datasets encompassing real and synthetic videos.
Abstract
Abstract: The emergence of deepfake technology presents a profound challenge to the integrity and trustworthiness of multimedia content online. To address this issue, this study proposes a novel deepfake detection system that integrates a hybrid Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) architecture. The proposed system employs a multi-faceted approach to training by utilizing several diverse datasets encompassing real and synthetic videos. This comprehensive training strategy ensures that the model learns robust features representative of both authentic and manipulated content across various contexts and scenarios. By incorporating a range of datasets, including those specifically curated to represent different deepfake generation techniques and quality levels, the model gains a more comprehensive understanding of the intricate nuances present in manipulated videos. During the training phase, the CNN component extracts high-level spatial features from individual frames of the input videos, effectively capturing visual patterns indicative of deepfake manipulation.