Abstract :
[en] In this paper, we investigate the suitability of current multi-label classification approaches for deepfake detection. With the recent advances in generative modeling, new deepfake detection methods have been proposed. Nevertheless, they mostly formulate this topic as a binary classification problem, resulting in poor explainability capabilities. Indeed, a forged image might be induced by multi-step manipulations with different properties.
For a better interpretability of the results, recognizing the nature of these stacked manipulations is highly relevant. For that reason, we propose to model deepfake detection as a multi-label classification task, where each label corresponds to a specific kind of manipulation. In this context, state-of-the-art multi-label image classification methods are considered. Extensive experiments are performed to assess the practical use case of deepfake detection.
Commentary :
This work is supported by the Luxembourg National Research Fund, under the BRIDGES2021/IS/16353350/FaKeDeTeR and UNFAKE, ref.16763798 projects, and by Post Luxembourg.
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