Identity verification platform Sumsub has launched its upgraded Adaptive Deepfake Detector that effectively spots emerging types of sophisticated fraud thanks to its machine learning ( ML )-driven detection tool with instant online self-learning upgrades.

The key differentiator of the upgraded tool, the platform points out, is its detection accuracy, which stems from non-stop model learning from fraud signals across multiple layers, allowing it to adapt within hours, not weeks or months.

The need for such an approach gained urgency in the last couple of years when it became obvious that the arms race between online fraudsters and security teams entered a new phase of high-level sophistication and speed.

From a technical standpoint, the upgraded detector:

• Continuously learns new patterns, including emerging deepfake types or injection methods, immediately incorporating them into the known threats list
• Signals are collected from multiple sources, not on a single anomaly vector. The multilayered fraud detection system analyzes documents, geolocation, IP address, device signals, facial biometrics ( liveness ) data, and cross-checks verification information from multiple users to spot fraudulent network activity
• Within each new observation, the model adjusts its parameters with no manual retraining required
• The detector’s decision boundary shifts to account for evolving threats, pushing the average detection accuracy close to 100%.

“In 2026, the threat landscape has evolved, demanding risk management teams to respond with the next-generation fraud prevention models,” adds Nikita Marshalkin, the platform’s ML head. “Modern deepfakes can no longer be detected by the human eye, and decision-making should be based on multiple signal analysis in real time.”