  The recent wave of AI research has enabled a new brand of synthetic media,
called deepfakes. Deepfakes have impressive photorealism, which has generated
exciting new use cases but also raised serious threats to our increasingly
digital world. To mitigate these threats, researchers have tried to come up
with new methods for deepfake detection that are more effective than
traditional forensics and heavily rely on deep AI technology. In this paper,
following up on encouraging prior work for deepfake detection with attribution
and ensemble techniques, we explore and compare multiple designs for ensemble
detectors. The goal is to achieve robustness and good generalization ability by
leveraging ensembles of models that specialize in different manipulation
categories. Our results corroborate that ensembles can achieve higher accuracy
than individual models when properly tuned, while the generalization ability
relies on access to a large number of training data for a diverse set of known
manipulations.
