How do I know if the deepfakes hitting our platform are sophisticated enough to fool our current biometric systems?

You can gauge whether deepfakes are fooling your biometric systems by reviewing false acceptance rates, manual review escalations, and cases where identities pass checks but later result in confirmed fraud. Deepfakes that bypass basic face matching or simple liveness checks often leave subtle signals that advanced biometric analysis and presentation attack detection can uncover.

What specific synthetic media attack techniques should I be watching for that could slip past our document verification process?

Synthetic media attacks that slip past document verification often involve high-resolution ID image manipulation, AI-generated faces inserted into legitimate document templates, and subtle digital retouching that preserves visible security features. Fraudsters may also pair authentic documents with synthetic or altered selfies to create convincing but fake identities that pass surface-level checks. Watching for inconsistencies between document structure, image artifacts, and biometric signals is critical to detecting these attacks before they move downstream.

Can our existing identity verification vendor actually detect presentation attacks, or are we just hoping they can?

The only way to know is to verify whether your vendor has explicitly tested and certified their biometric systems against presentation attacks, rather than relying on general claims about fraud detection. Look for evidence of formal Presentation Attack Detection testing, such as iBeta certifications, and ask how their models identify deepfakes, replays, and other spoofing techniques. If a vendor cannot clearly explain how they detect these attacks and validate performance, you are likely relying on hope rather than proven capability.

How do I test whether our current fraud prevention stack can catch AI-generated faces and documents before a real attack happens?

You can test your fraud prevention stack by running controlled simulations using known AI-generated faces, deepfake videos, and manipulated documents alongside real customer samples. Compare how often these synthetic inputs pass verification, trigger step-ups, or require manual review, and track false acceptance rates across each layer of your stack. This type of parallel testing exposes blind spots early and helps you validate whether your controls can detect synthetic media attacks before real fraud occurs.