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In this paper, we propose to utilize Automated Machine Learning to adaptivelysearch a neural architecture for deepfake detection. This is the first time toemploy automated machine learning for deepfake detection. Based on our exploredsearch space, our proposed method achieves competitive prediction accuracycompared to previous methods. To improve the generalizability of our method,especially when training data and testing data are manipulated by differentmethods, we propose a simple yet effective strategy in our network learningprocess: making it to estimate potential manipulation regions besidespredicting the real/fake labels. Unlike previous works manually design neuralnetworks, our method can relieve us from the high labor cost in networkconstruction. More than that, compared to previous works, our method dependsmuch less on prior knowledge, e.g., which manipulation method is utilized orwhere exactly the fake image is manipulated. Extensive experimental results ontwo benchmark datasets demonstrate the effectiveness of our proposed method fordeepfake detection.

Automated Deepfake Detection