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Enabled by recent improvements in generation methodologies, DeepFakes havebecome mainstream due to their increasingly better visual quality, the increasein easy-to-use generation tools and the rapid dissemination through socialmedia. This fact poses a severe threat to our societies with the potential toerode social cohesion and influence our democracies. To mitigate the threat,numerous DeepFake detection schemes have been introduced in the literature butvery few provide a web service that can be used in the wild. In this paper, weintroduce the MeVer DeepFake detection service, a web service detecting deeplearning manipulations in images and video. We present the design andimplementation of the proposed processing pipeline that involves a modelensemble scheme, and we endow the service with a model card for transparency.Experimental results show that our service performs robustly on the threebenchmark datasets while being vulnerable to Adversarial Attacks. Finally, weoutline our experience and lessons learned when deploying a research systeminto production in the hopes that it will be useful to other academic andindustry teams.

The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in the Wild