Sumsub has launched an upgraded deepfake detection solution that uses machine learning to identify emerging AI-generated fraud threats faster.
The Adaptive Deepfake Detector updates continuously instead of relying on scheduled model upgrades, which can take weeks or months to roll out.
The launch comes as fraudsters use more advanced deepfake images, voices, videos and injection methods to bypass online verification checks.
Multi-step attacks rose by 180% in 2025, reaching 28% of all fraud detected on Sumsub’s platform globally.
The upgraded deepfake detection model by Sumsub analyses documents, geolocation, IP addresses, device signals, facial biometrics, liveness checks and verification patterns across multiple users to detect possible fraud networks.
Nikita Marshalkin, Head of Machine Learning at Sumsub, said,
“Modern deepfakes can no longer be detected by the human eye, and decision-making should be based on multiple signal analysis in real time.
That’s why we launched our upgraded Deepfake Detector, offering clients not just a tool, but rather an online learning system that combines advanced document checks, device intelligence, and fraudulent networks analysis to complement deepfake detection capabilities.”
The solution also checks for presentation attacks, injection attempts, third-party involvement and poor-quality verification inputs such as motion blur, glare or unusual facial expressions.
Sumsub added that the model can adjust to new fraud patterns without manual retraining as new threat signals enter the system.
Featured image: Edited by Fintech News Singapore, based on image by awarecreativestudio via Magnific
