  The proliferation of DeepFake technology is a rising challenge in today's
society, owing to more powerful and accessible generation methods. To counter
this, the research community has developed detectors of ever-increasing
accuracy. However, the ability to explain the decisions of such models to users
is lacking behind and is considered an accessory in large-scale benchmarks,
despite being a crucial requirement for the correct deployment of automated
tools for content moderation. We attribute the issue to the reliance on
qualitative comparisons and the lack of established metrics. We describe a
simple set of metrics to evaluate the visual quality and informativeness of
explanations of video DeepFake classifiers from a human-centric perspective.
With these metrics, we compare common approaches to improve explanation quality
and discuss their effect on both classification and explanation performance on
the recent DFDC and DFD datasets.
