Sumsub's Adaptive Deepfake Detection Aims to Outpace Fraud Arms Race
Event summary
- Sumsub launched 'Adaptive Deepfake Detector,' a new fraud prevention model featuring instant online self-learning updates.
- The share of multi-step fraud attacks increased by 180% in 2025, reaching 28% of all fraud detected by Sumsub globally.
- Traditional deepfake detection methods are failing due to the speed of evolving fraud techniques, with a gap between model updates lasting weeks or months.
- Sumsub's new detector aims to adapt to emerging threats within hours, leveraging multiple data layers beyond visual content.
- Nikita Marshalkin, Head of Machine Learning at Sumsub, emphasizes the need for real-time, multi-signal analysis in fraud prevention.
The big picture
The rapid evolution of AI-generated deepfakes is creating a significant challenge for digital businesses, forcing a shift from reactive, periodic fraud prevention to proactive, real-time solutions. Sumsub's Adaptive Deepfake Detector represents a move towards a more dynamic security posture, but the ongoing arms race between fraudsters and security teams will require continuous innovation and investment. The increasing prevalence of multi-step attacks underscores the need for holistic verification approaches that extend beyond simple visual checks.
What we're watching
- Adoption Rate
- The speed at which Sumsub's existing client base adopts the Adaptive Deepfake Detector will be a key indicator of its perceived value and impact on fraud reduction.
- Competitive Response
- Other verification platform providers will likely accelerate their own real-time adaptation capabilities, potentially leading to a price war or feature parity.
- Efficacy
- The long-term effectiveness of the 'online learning' model will depend on its ability to stay ahead of increasingly sophisticated fraud techniques and avoid false positives.
