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The recent wave of AI research has enabled a new brand of synthetic media,called deepfakes. Deepfakes have impressive photorealism, which has generatedexciting new use cases but also raised serious threats to our increasinglydigital world. To mitigate these threats, researchers have tried to come upwith new methods for deepfake detection that are more effective thantraditional forensics and heavily rely on deep AI technology. In this paper,following up on encouraging prior work for deepfake detection with attributionand ensemble techniques, we explore and compare multiple designs for ensembledetectors. The goal is to achieve robustness and good generalization ability byleveraging ensembles of models that specialize in different manipulationcategories. Our results corroborate that ensembles can achieve higher accuracythan individual models when properly tuned, while the generalization abilityrelies on access to a large number of training data for a diverse set of knownmanipulations.

Investigation of ensemble methods for the detection of deepfake face manipulations