  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. Experimental results
show that the proposed pre-processing approach leads to considerable
improvements in the performance of detection models, and the proposed
prediction aggregation scheme further boosts the detection efficiency in cases
where there are multiple faces in a video.
