Sherlock applies a multimodal adversarial ML approach to detect interview fraud, combining signals from device activity, audio environments, and candidate behavior into a unified classifier. Rather than relying on rule-based triggers, Sherlock models natural versus adversarial interaction patterns, enabling it to recognize when behavior has been subtly engineered to evade detection. The system is retrained on adversarially enriched datasets at regular intervals, which has recently raised overall detection accuracy from ~85% to over 97%. This continual refinement ensures resilience against evolving cheating tactics while maintaining enterprise-grade reliability.