Transfer Learning for Deepfake Detection in Static Facial Images

Abstract

Digital authentication systems that rely on biometric recognition are especially vulnerable to deepfake attacks, which can be used to impersonate legitimate users and bypass security protocols. As deepfake attacks become increasingly sophisticated, detection methods must evolve rapidly. In this paper, we propose the usage of transfer learning instead of standard deep learning to provide a fast response to novel threats. We evaluate 12 approaches, combining three deep neural networks as feature extractors with four traditional machine learning algorithms as classifiers. Finally, the best-performing model, i.e. ConvNeXt with a support vector classifier, is fine-tuned and evaluated on a real-world dataset, demonstrating strong performance.

Digital authentication systems that rely on biometric recognition are especially vulnerable to deepfake attacks, which can be used to impersonate legitimate users and bypass security protocols. As deepfake attacks become increasingly sophisticated, detection methods must evolve rapidly. In this paper, we propose the usage of transfer learning instead of standard deep learning to provide a fast response to novel threats. We evaluate 12 approaches, combining three deep neural networks as feature extractors with four traditional machine learning algorithms as classifiers. Finally, the best-performing model, i.e. ConvNeXt with a support vector classifier, is fine-tuned and evaluated on a real-world dataset, demonstrating strong performance.