Deep Learning Approach to Detect Fake Video on Raspberry PI

Abstract

Abstract: This project proposes a deep learning-based approach for real-time detection of fake videos on a resource-constrained device, specifically the Raspberry Pi. The solution combines the power of computer vision and recurrent neural networks to discern manipulated content from authentic videos effectively. The methodology involves using a pre-trained ResNeXt model for feature extraction, capturing spatial information from each video frame. These features are then fed into a Long Short-Term Memory (LSTM) network, allowing the model to understand and exploit temporal dependencies within the sequence of frames. The LSTM network learns patterns and nuances indicative of authentic or manipulated video content. The training process involves a carefully curated dataset containing both real and fake videos. The model is fine-tuned to optimize its performance, and metrics such as accuracy, precision, recall, and F1 score are employed for evaluation. To accommodate the constraints of the Raspberry Pi, the model is further optimized through techniques such as quantization, ensuring a balance between model size and inference accuracy. The final model is deployed on the Raspberry Pi, with a user-friendly interface capturing video frames in real-time. The system provides instantaneous feedback, indicating whether the observed video content is genuine or manipulated. This project contributes to the growing field of deepfake detection while addressing the challenges of implementing sophisticated models on edge devices. The combination of ResNeXt and LSTM offers a robust solution for discerning manipulated videos, making it suitable for real-world applications where computational resources are limited.