Method and system for probably robust classification with detection of adversarial examples

A system including a machine-learning network, comprising:

• an input interface configured to receive input data from a sensor, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor;
• a processor, in communication with the input interface, wherein the processor is programmed to:
• receive an input data from a sensor, wherein the input data is indicative of image, radar, sonar, or sound information;
• train a classifier, wherein the classifier includes a plurality of classes, including an additional abstain class, wherein the abstain class is determined in response to at least bounding input data including one or more perturbations;
• and
• output a trained classifier configured to detect the additional abstain class in response in response to exceeding a convergence threshold.

A computer-program product storing instructions which, when executed by a computer, cause the computer to:

• receive input data from a sensor, wherein the sensor includes a video, radar, LiDAR, sound, sonar, ultrasonic, motion, or thermal imaging sensor, wherein the input data is indicative of an image;
• obtain a worst case bound on a classification error and loss associated with perturbed versions of the input data, utilizing at least bounding of one or more hidden layer values;
• train a classifier of a machine-learning network, wherein the classifier includes a plurality of classes, including an additional abstain class, wherein the abstain class is determined in response to at least bounding input data including one or more perturbations;
• and
• output a trained classifier configured to detect the additional abstain class in response to exceeding a convergence threshold.