Towards Robust and Generalizable Deepfake Detection: A Multi-Model Neural Network Approach

Abstract

The rapid evolution of deepfake technology poses significant threats to digital media integrity, privacy, and security. Traditional deepfake detection methods, such as frame-based analysis and conventional classifiers, struggle to counter increasingly sophisticated generative adversarial networks (GANs) and advanced manipulation techniques. This paper presents an AI-driven deepfake detection system that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Temporal Convolutional Networks (TCNs) to enhance detection accuracy and robustness. By leveraging spatial and temporal inconsistencies within manipulated videos, our approach outperforms conventional methods, particularly in real-world scenarios with diverse datasets and adversarial perturbations. We evaluate our model against benchmark datasets, demonstrating superior performance in detecting face-swapped deepfakes with high precision. Additionally, we discuss the societal implications of deepfake proliferation and highlight the need for ethical deployment of detection technologies. Our proposed framework contributes to the advancement of deepfake forensics, providing a scalable and effective solution to combat digital deception.