  Deep learning technology has made it possible to generate realistic content
of specific individuals. These `deepfakes' can now be generated in real-time
which enables attackers to impersonate people over audio and video calls.
Moreover, some methods only need a few images or seconds of audio to steal an
identity. Existing defenses perform passive analysis to detect fake content.
However, with the rapid progress of deepfake quality, this may be a losing
game.
  In this paper, we propose D-CAPTCHA: an active defense against real-time
deepfakes. The approach is to force the adversary into the spotlight by
challenging the deepfake model to generate content which exceeds its
capabilities. By doing so, passive detection becomes easier since the content
will be distorted. In contrast to existing CAPTCHAs, we challenge the AI's
ability to create content as opposed to its ability to classify content. In
this work we focus on real-time audio deepfakes and present preliminary results
on video.
  In our evaluation we found that D-CAPTCHA outperforms state-of-the-art audio
deepfake detectors with an accuracy of 91-100% depending on the challenge
(compared to 71% without challenges). We also performed a study on 41
volunteers to understand how threatening current real-time deepfake attacks
are. We found that the majority of the volunteers could not tell the difference
between real and fake audio.
