A self-supervised BEiT model with a novel hierarchical patchReducer for efficient facial deepfake detection

The spread of deepfake technology has become a growing concern, especially with the rapid advancement of generative models. This progress has made it increasingly difficult to distinguish between real and fake facial videos. This poses serious threats to security, privacy, and the spread of misinformation. Despite the existence of deepfake detection models, many suffer from high computational costs. This makes them impractical for deployment in resource-constrained environments. To address this challenge, this paper proposes a solution that accurately identifies deepfakes while reducing computational complexity. To achieve this, a deepfake detection system using an improved version of a Self-Supervised BEiT, called BEiT-HPR (Hierarchical PatchReducer), is proposed. The enhancement adds a Hierarchical PatchReducer layer to reduce the number of patches in successive encoder blocks. This reduces computational complexity while maintaining high detection accuracy. Additionally, training speed increases by over 50%, while the number of parameters is reduced by 63.4%. The BEiT-HPR model was tested using three publicly available benchmark datasets: FaceForensics++ (FF++), Celeb-DF, and the Deepfake Detection Dataset (DFD). The evaluation results revealed that reducing the model complexity by 62.2% allowed the proposed model to achieve an accuracy of 83.92% on FF++, 97.59% on Celeb-DF, and 98.25% on DFD. These findings emphasize the importance of computationally efficient deepfake detection methods that maintain high detection accuracy while reducing the burden of heavy computation. Therefore, it offers a scalable solution for identifying deepfakes across diverse datasets.