Investigating the Impact of Pre-processing and Prediction Aggregation on the DeepFake Detection Task

Understand

Recent advances in content generation technologies (widely known as DeepFakes) along with the online proliferation of manipulated media content render the detection of such manipulations a task of increasing importance.

• Even though there are many DeepFake detection methods, only a few focus on the impact of dataset preprocessing and the aggregation of frame-level to video-level prediction on model performance.
• In this paper, we propose a pre-processing step to improve the training data quality and examine its effect on the performance of DeepFake detection.
• We also propose and evaluate the effect of video-level prediction aggregation approaches.