JUST LIKE ANY lock can be picked, any biometric scanner can be fooled. Researchers have shown for years that the popular fingerprint sensors used to guard smartphones can be tricked sometimes, using a lifted print or a person’s digitized fingerprint data. But new findings from computer scientists at New York University’s Tandon School of Engineering could raise the stakes significantly. The group has developed machine learning methods for generating fake fingerprints—called DeepMasterPrints—that not only dupe smartphone sensors, but can successfully masquerade as prints from numerous different people. Think of it as a skeleton key for fingerprint-protected devices.

The work builds on research into the concept of a “master print” that combines common fingerprint traits. In initial tests last year, NYU researchers explored master prints by manually identifying various features and characteristics that could combine to make a fingerprint that authenticates multiple people. The new work vastly expands the possibilities, though, by developing machine learning models that can churn out master prints.

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