A Universal Facial ID 'Master Key' Through Machine Learning

Italian researchers have developed a method by which it's possible to bypass facial recognition ID checks for any user, in systems that have been trained on a Deep Neural Network (DNN). The approach works even for target users that enrolled into the system after the DNN was trained, and potentially enables the providers of end-to-end encrypted systems to unlock the data of any user via facial ID authentication, even in scenarios where that is not supposed to be possible. The paper, from the Department of Information Engineering and Mathematics at the University of Siena, outlines a possible compromising of user-encrypted facial ID verification systems by introducing'poisoned' facial images into the training data sets that power them. Once introduced into the training set, the owner of the poisoned face is able to unlock the account of any user through facial ID authentication. Images used in the'Master Key' system, to be included at the training phase.