Video deepfake detection using a hybrid CNN-LSTM-Transformer model for identity verification

Abstract

The proliferation of deepfake technology poses significant challenges due to its potential for misuse in creating highly convincing manipulated videos. Deep learning (DL) techniques have emerged as powerful tools for analyzing and identifying subtle inconsistencies that distinguish genuine content from deepfakes. This paper introduces a novel approach for video deepfake detection that integrates 3D Morphable Models (3DMMs) with a hybrid CNN-LSTM-Transformer model, aimed at enhancing detection accuracy and efficiency. Our model leverages 3DMMs for detailed facial feature extraction, a CNN for fine-grained spatial analysis, an LSTM for short-term temporal dynamics, and a Transformer for capturing long-term dependencies in sequential data. This architecture effectively addresses critical challenges in current detection systems by handling both local and global temporal information. The proposed model employs an identity verification approach, comparing test videos with reference videos containing genuine footage of the individuals. Trained and validated on the VoxCeleb2 dataset, with further testing on three additional datasets, our model demonstrates superior performance to existing state-of-the-art methods, maintaining robustness across different video qualities, compression levels and manipulation types. Additionally, it operates efficiently in time-sensitive scenarios, significantly outperforming existing methods in inference speed. By relying solely on pristine, unmanipulated data for training, our approach enhances adaptability to new and sophisticated manipulations, setting a new benchmark for video deepfake detection technologies. This study not only advances the framework for detecting deepfakes but also underscores its potential for practical deployment in areas critical for digital forensics and media integrity.