  Due to the rising threat of deepfakes to security and privacy, it is most
important to develop robust and reliable detectors. In this paper, we examine
the need for high-quality samples in the training datasets of such detectors.
Accordingly, we show that deepfake detectors proven to generalize well on
multiple research datasets still struggle in real-world scenarios with
well-crafted fakes. First, we propose a novel autoencoder for face swapping
alongside an advanced face blending technique, which we utilize to generate 90
high-quality deepfakes. Second, we feed those fakes to a state-of-the-art
detector, causing its performance to decrease drastically. Moreover, we
fine-tune the detector on our fakes and demonstrate that they contain useful
clues for the detection of manipulations. Overall, our results provide insights
into the generalization of deepfake detectors and suggest that their training
datasets should be complemented by high-quality fakes since training on mere
research data is insufficient.
