Audio Deepfake Detection Benchmark Results: How 8 Systems Performed in 2026

In May 2026, Podonos conducted an impartial benchmark to evaluate eight audio deepfake detection systems, including both commercial APIs and open-source models, using a private-label test set that mimicked real-world conditions. The results highlighted significant disparities in performance, with Resemble AI achieving the highest accuracy at 98.1% and Aurigin AI following at 96.8%, while open-source models lagged due to outdated training datasets. The study emphasized the importance of real-time performance and low false negative rates in production environments, noting that detection systems must be evaluated on their ability to handle modern attack distributions and operational requirements. Additionally, the benchmark underscored the necessity for systems to maintain sub-second latency and RTF below 1.0 for live applications. It stressed the importance of regularly updating detection models to keep pace with rapidly evolving threats and recommended evaluating the false positive and false negative rate tradeoffs and threshold customization capabilities of detection vendors to meet specific deployment needs.