Enhancing Deepfake Detection Through Dynamics of Facial Expressions

The rapid evolution of deepfake technology poses significant threats to digital security, media integrity, and public trust, necessitating the development of robust detection frame-works. Traditional deepfake detection methods primarily rely on pixel inconsistencies, frequency-domain analysis, or handcrafted features, but these approaches are increasingly vulnerable to advanced generative models that produce high-fidelity manipulations. In this study, we introduce MADDM, a Masked Autoencoder-based deepfake detection model that leverages facial expression dynamics to identify inconsistencies in muscle coordination—an aspect that remains challenging for deepfake generators to replicate accurately. Our model is trained in a self-supervised manner, first learning natural facial expressions from real datasets and then detecting anomalies in synthetic videos by reconstructing masked facial regions. Evaluations on Celeb-DF, DFDC, and FaceForensics++ datasets demonstrate that MADDM significantly outperforms existing detection methods, achieving an average accuracy of 81.1%, with state-of-the-art performance on Celeb-DF (86.3%). Further analysis through intra-dataset and cross-dataset testing confirms the model’s superior generalization capabilities. The results highlight the potential of expression-based deepfake detection as a powerful and scalable solution for digital forensics and misinformation control. Future research should explore real-time implementation, transformer-based optimizations, and adversarial training strategies to enhance detection efficiency against evolving deepfake techniques.