Certifying attack resistance of convolutional neural networks
Thursday, January 31, 2019 - 11:00
in Health & Medicine
When shopping for a watch, you may notice its water resistance rating, which indicates that the watch is warranted to be waterproof to a certain level. What about your neural network? Can one ensure a neural network is "attack proof", meaning that its functionality is robust against adversarial perturbations? If so, how can this be quantified with an attack resistance number? At AAAI 2019, our group of researchers from MIT and IBM Research proposes an efficient and effective method for certifying attack resistance of convolutional neural networks to given input data. This paper is selected for oral presentation at AAAI 2019 (January 30, 2:00-3:30 pm @ coral 1).