{"id":84818,"date":"2018-11-17T01:42:26","date_gmt":"2018-11-17T09:42:26","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2018\/11\/researchers-create-master-fingerprints-to-unlock-phones"},"modified":"2018-11-17T01:42:26","modified_gmt":"2018-11-17T09:42:26","slug":"researchers-create-master-fingerprints-to-unlock-phones","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2018\/11\/researchers-create-master-fingerprints-to-unlock-phones","title":{"rendered":"Researchers Create \u2018Master Fingerprints\u2019 to Unlock Phones"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/researchers-create-master-fingerprints-to-unlock-phones.jpg\"><\/a><\/p>\n<p>Biometric features like fingerprint sensors and iris scanners have made it easier to securely unlock phones, but they may never be as secure as a good old-fashioned password. Researchers have repeatedly worked out methods to impersonate registered users of biometric devices, but now a team from New York University and the University of Michigan has gone further. The team managed to create so-called \u201cDeepMasterPrints\u201d that can <a href=\"https:\/\/motherboard.vice.com\/en_us\/article\/bjenyd\/researchers-created-fake-master-fingerprints-to-unlock-smartphones\" target=\"_blank\" rel=\"noopener\">fool a sensor<\/a> without a sample of the real user\u2019s fingerprints.<\/p>\n<p>Past attempts to bypass biometric systems usually involve getting access to a registered individual\u2019s data \u2014 that could be a copy of their fingerprint or a 3D scan of their face. DeepMasterPrints involves generating an entirely new fingerprint from a mountain of data that\u2019s close enough to fool the sensor. Like so many research projects these days, the team <a href=\"https:\/\/arxiv.org\/abs\/1705.07386\" target=\"_blank\" rel=\"noopener\">used neural networks<\/a> to do the heavy lifting.<\/p>\n<p>The process started with feeding fingerprints from 6,000 people into a neural network in order to train it on what a human fingerprint looks like. A neural network is composed of a series of nodes that process data. It feeds forward into additional \u201clayers\u201d of nodes if the output meets a certain threshold. Thus, you can train the network to get the desired output. In this case, the researchers used a \u201cgenerative adversarial network\u201d to tune the system\u2019s ability to generate believable fingerprints. The network used its understanding of prints to make one from scratch, and then a second network would determine if they were real or fake. If the fingerprints didn\u2019t pass muster, the network could be re-tuned to try again.<\/p>\n<p><!-- Link: <a href=\"https:\/\/www.extremetech.com\/extreme\/280713-researchers-create-master-fingerprints-to-unlock-phones?source=science\">https:\/\/www.extremetech.com\/extreme\/280713-researchers-creat...ce=science<\/a> --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Biometric features like fingerprint sensors and iris scanners have made it easier to securely unlock phones, but they may never be as secure as a good old-fashioned password. Researchers have repeatedly worked out methods to impersonate registered users of biometric devices, but now a team from New York University and the University of Michigan has [\u2026]<\/p>\n","protected":false},"author":396,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1512,1412,6],"tags":[],"class_list":["post-84818","post","type-post","status-publish","format-standard","hentry","category-mobile-phones","category-privacy","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/84818","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/396"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=84818"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/84818\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=84818"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=84818"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=84818"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}