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Automated Deepfake Detection
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TLDR
In this article, a multi-task strategy was proposed to estimate potential manipulation regions in given samples as well as predict whether the samples are real or not. But, their method depends much less on prior knowledge, such as no need to know which manipulation method is utilized and whether it is utilized already.Abstract:
In this paper, we propose to utilize Automated Machine Learning to
automatically search architecture for deepfake detection. Unlike previous
works, our method benefits from the superior capability of deep learning while
relieving us from the high labor cost in the manual network design process. It
is experimentally proved that our proposed method not only outperforms previous
non-deep learning methods but achieves comparable or even better prediction
accuracy compared to previous deep learning methods. To improve the generality
of our method, especially when training data and testing data are manipulated
by different methods, we propose a multi-task strategy in our network learning
process, making it estimate potential manipulation regions in given samples as
well as predict whether the samples are real. Comparing to previous works using
similar strategies, our method depends much less on prior knowledge, such as no
need to know which manipulation method is utilized and whether it is utilized
already. Extensive experimental results on two benchmark datasets demonstrate
the effectiveness of our proposed method on deepfake detection.read more
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