Using Adversarial Machine Learning, Researchers Look to Foil Facial Recognition

Facial recognition is quickly becoming a disruptive technology with few limits imposed by privacy policy. Academic researchers, however, have found ways to -- at least temporarily -- cause problems for certain classes of facial-recognition algorithms, taking advantages of weaknesses in the training algorithm or the resultant recognition model. Last week, a team of computer-science researchers at the National University of Singapore (NUS) published a technique that locates the areas of an image where changes can best disrupt image-recognition algorithms, but where those changes are least noticeable to humans. The technique is general in that it can be used to develop an attack against other machine-learning (ML) algorithms, but the researchers only developed a specific instance, says Mohan Kankanhalli, a professor in the NUS Department of Computer Science and co-author of a paper on the adversarial attack. "Currently, we need to know the class [of algorithm] and can develop a solution for that," he says.