Midv250 Verified <99% VALIDATED>

If a vendor says they are MIDV250 Verified, ask for their specific Equal Error Rate (EER) on the morphing subset of the dataset. The true standard is an EER of <0.1% for bona fide presentations and <5% for morphing attacks.

Keywords: midv250 verified, identity document verification, morphing attack detection, KYC compliance, AML video verification, MIDV-250 dataset, liveness detection. midv250 verified

The cost of a single morphing attack bypass—a criminal opening a line of credit using a fake ID that looks 82% like them and 18% like a stolen identity—far outweighs the cost of upgrading to a MIDV250 Verified engine. If a vendor says they are MIDV250 Verified,

To earn the badge, a verification engine must achieve three specific outcomes: 1. Sub-Second Liveness Detection The system must detect a "live" document versus a screen replay or a printed copy within 900 milliseconds. MIDV-250 Verified systems excel at distinguishing a real polycarbonate ID card from a high-resolution smartphone photo of that card. 2. Morphing Attack Potential (MAP) Score < 5% Morphing is the biggest security threat of the decade. A "Verified" system must reject identity documents where the portrait photo has a MAP score exceeding 5% (meaning there is a 1 in 20 chance the photo is a composite of two people). Standard (non-verified) systems typically allow a 15-20% margin. 3. OCR Accuracy in Degraded Video Optical Character Recognition (OCR) on static images is easy. OCR on video is hard. The MIDV-250 standard requires 99.2% character accuracy reading the MRZ and document numbers even when the video source has motion blur (simulating a user holding a shaky phone). Why "MIDV250 Verified" Matters for Compliance For regulated industries (Banking, Fintech, Crypto, Gambling, and Age-Restricted eCommerce), regulators are no longer satisfied with "we take a picture of an ID." They demand proof that the system can resist generative AI attacks. The cost of a single morphing attack bypass—a

MIDV is a series of academic and industrial datasets (MIDV-500, MIDV-2019, and most critically, MIDV-250) designed to test the robustness of automated identity document recognition systems. Unlike static image datasets, MIDV leverages .