Comprehensive Analysis of Deepfake Detection Models

Deepfake technology being constantly developed at a fast pace, poses a great danger to the authenticity of media content, and thus prompt detection frameworks are required. This paper reviews the state of the art in deepfake detection systems focusing on four shallow deep learning CNN models, XceptionNet, EfficientNetV2M, EfficientNetV2S, InceptionResNetV2. This paper focuses on the datasets and model architectures. The selected metrics are explained which concretize the relevance and versatility of these approaches. The research applies a deep learning method that is resource efficient and cost effective, to resolve important problems such as imbalance in data. The tested cases prove the effectiveness of the models in deepfake detection in regard to several cases. The XceptionNet model recorded 98% accuracy in the classification task using the CelebDF-V2 dataset, while the InceptionResNetV2 model achieved 94% accuracy on the FaceForensics++ dataset.