  Enabled by recent improvements in generation methodologies, DeepFakes have
become mainstream due to their increasingly better visual quality, the increase
in easy-to-use generation tools and the rapid dissemination through social
media. This fact poses a severe threat to our societies with the potential to
erode social cohesion and influence our democracies. To mitigate the threat,
numerous DeepFake detection schemes have been introduced in the literature but
very few provide a web service that can be used in the wild. In this paper, we
introduce the MeVer DeepFake detection service, a web service detecting deep
learning manipulations in images and video. We present the design and
implementation of the proposed processing pipeline that involves a model
ensemble scheme, and we endow the service with a model card for transparency.
Experimental results show that our service performs robustly on the three
benchmark datasets while being vulnerable to Adversarial Attacks. Finally, we
outline our experience and lessons learned when deploying a research system
into production in the hopes that it will be useful to other academic and
industry teams.
