Deepfake Detection Using a Hybrid Deep Learning Approach with Swin Transformers and ConvNeXt

Abstract

The rapid advancement of artificial intelligence has led to the proliferation of deep-fake technology, which poses significant challenges to digital security and trust. As deepfakes become increasingly sophisticated, there is an urgent need for effective detection methods that can accurately identify manipulated media across various platforms and contexts. Deepfakes are outcomes of advanced Artificial Intelligence algorithms such as Generative Adversarial Networks, and have become a danger to digital integrity, personal privacy, and public trust. With continuous advancements in the techniques for generating deepfakes, conventional methods of detection based on the artifact analysis of visuals and inconsistencies in physiological signals have started to wear out. This paper presents a hybrid deep learning model that works between Swin Transformers and ConvNeXt architectures for better detection. The proposed model achieves better detection accuracy and robustness by leveraging Swin Transformers' hierarchical feature extraction capabilities and the efficient processing strengths of ConvNeXt. Obtained results on the "Deepfake and Real Images" dataset demonstrated performance of 95.6% accuracy, 97.4% precision, 93.7% recall, and a 95.2% F1 score. The hybrid model is more effective than existing ones, demonstrating their potential for concrete application in social media platforms, news agencies, and digital forensics to help fight misinformation and preserve digital trust.