DeepFakes has become a social problem by slandering the public image of people.
The current and specialized DeepFake detection methods use Hand-Designed Deep
Neural Networks as backbone, which requires a lot of designing effort. We propose
to use a Neural Architecture Search algorithm for DeepFake detection, which aims to
overcome the drawbacks of the Hand-Designed approaches, and has not been proved
yet on DeepFake detection. Additionally, in this work is analyzed the robustness of
the Architecture ...
DeepFakes has become a social problem by slandering the public image of people.
The current and specialized DeepFake detection methods use Hand-Designed Deep
Neural Networks as backbone, which requires a lot of designing effort. We propose
to use a Neural Architecture Search algorithm for DeepFake detection, which aims to
overcome the drawbacks of the Hand-Designed approaches, and has not been proved
yet on DeepFake detection. Additionally, in this work is analyzed the robustness of
the Architecture Search process. We yield comparable results to DeepFake detection
state-of-the-art, that are highly specialized, by adapting Progressive Differential
Architecture Search to DeepFake detection for the first time, using novel techniques
such as data augmentation, multi-label classification and an architecture search
process in the particular domain to improve in terms of performance. Finally, We
have found that the data requirements to obtain a stable architecture are not very
high.
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