Deep Learning Deepfake Detection
The Two-Stream models, which concurrently process spatial and temporal features, demonstrate superior performance in identifying deepfake videos, thereby underscoring the significance of temporal dynamics in deepfake detection technology.
Abstract
In the pursuit of effective deepfake detection, this study delves into the comparative effectiveness of various deep learning architectures across multiple levels of granular-ity—from individual frame analysis to whole-video synthesis. We evaluate a multitude of models including Baseline 2D CNNs, Two-Stream CNNs for both per-frame and whole-voideo analysis, CNN-RNN hybrids, and 3D CNNs. Our results highlight the importance of integrating both spatial and temporal data to enhance detection accuracy. The Two-Stream models, which concurrently process spatial and temporal features, demonstrate superior performance in identifying deepfake videos, thereby underscoring the significance of temporal dynamics in deepfake detection technology.