We present a novelty approach to deepfake detection. Deepfake is a type
of media, usually a picture or video, in which a part of the picture, most frequently face or body, has been digitally modified. Deepfakes are often used
with ill intentions, such as spreading misinformation or opinion formulation.
Modification of digital media usually leaves traces, a so-called digital artefacts. Artefacts can be defined as irregularities in digital media which are
unwanted consequences of modification. We present five methods for detecting deepfakes by detecting artefacts of generative adversarial networks. We
evaluate the presented methods on seven different deepfake databases which
are further divided into those that are primarily generated by a generative
adversarial network and those that are not. We show that the presented
methods achieve promising results on the prepared databases.
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