Enhancing practicality and efficiency of deepfake detection.

The proliferation of deepfake generation has become increasingly widespread. Current solutions for automatically detecting and classifying generated content require substantial computational resources, making them impractical for use by the average non-expert individual, particularly from edge computing applications. In this paper, we propose a series of techniques to accelerate the inference speed of deepfake detection on video data.