Anti-spoofing method and apparatus

This invention describes a method to determine if biometric information, such as a fingerprint or face scan, is real or forged. It uses a neural network to first get an initial indication of forgery, specifically by checking if an intermediate score falls within a certain range. Then, it extracts unique features from the biometric data and compares them to pre-existing real and fake examples. These two pieces of information are combined into a final score to decide if the biometric data is forged, with the calculation varying based on the initial indication.

Why it matters: Since 2022, the capabilities of neural networks for detecting subtle anomalies in biometric data have advanced significantly, driven by improved architectures and larger training datasets. This could make the multi-stage detection and scoring method described more robust and accurate to implement now.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedNeural network training, biometric feature extraction, software logic.

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