  Scammers are aggressively leveraging AI voice-cloning technology for social
engineering attacks, a situation significantly worsened by the advent of audio
Real-time Deepfakes (RTDFs). RTDFs can clone a target's voice in real-time over
phone calls, making these interactions highly interactive and thus far more
convincing. Our research confidently addresses the gap in the existing
literature on deepfake detection, which has largely been ineffective against
RTDF threats. We introduce a robust challenge-response-based method to detect
deepfake audio calls, pioneering a comprehensive taxonomy of audio challenges.
Our evaluation pitches 20 prospective challenges against a leading
voice-cloning system. We have compiled a novel open-source challenge dataset
with contributions from 100 smartphone and desktop users, yielding 18,600
original and 1.6 million deepfake samples. Through rigorous machine and human
evaluations of this dataset, we achieved a deepfake detection rate of 86% and
an 80% AUC score, respectively. Notably, utilizing a set of 11 challenges
significantly enhances detection capabilities. Our findings reveal that
combining human intuition with machine precision offers complementary
advantages. Consequently, we have developed an innovative human-AI
collaborative system that melds human discernment with algorithmic accuracy,
boosting final joint accuracy to 82.9%. This system highlights the significant
advantage of AI-assisted pre-screening in call verification processes. Samples
can be heard at https://mittalgovind.github.io/autch-samples/
