US 12,288,154 B2
Adaptive robustness certification against adversarial examples
Kevin Eykholt, White Plains, NY (US); Taesung Lee, Ridgefield, CT (US); Jiyong Jang, Chappaqua, NY (US); Shiqi Wang, Brooklyn, NY (US); and Ian Michael Molloy, Ridgefield, CT (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Dec. 7, 2020, as Appl. No. 17/113,927.
Prior Publication US 2022/0180172 A1, Jun. 9, 2022
Int. Cl. G06N 3/047 (2023.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/231 (2023.01); G06F 18/243 (2023.01); G06N 3/045 (2023.01); G06N 3/063 (2023.01); G06N 3/08 (2023.01)
CPC G06N 3/08 (2013.01) [G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/231 (2023.01); G06F 18/24323 (2023.01); G06N 3/063 (2013.01)] 29 Claims
OG exemplary drawing
 
1. A method of training and using a machine learning model, comprising:
obtaining class similarity data for a data set;
receiving a set of robustness criteria for the data set, wherein the set of robustness criteria includes at least first and second robustness criterion that differ from one another, wherein the set of robustness criteria increases as the class similarity decreases;
applying a smart label grouping algorithm to define the set of robustness criteria, wherein the smart label grouping algorithm uses a single classifier trained with a customized loss using an Inter-Group Robustness Prioritization (IGRP) scheme, wherein the customized loss comprises at least two types of relationships: an outer group loss and an inner group loss;
clustering dissimilar class data into different class groups, wherein the outer group loss enforces a strict robustness criterion between the different class groups and clustering similar class data into a same class group, wherein the inner group loss enforces a loose robustness criterion between the same class group;
training the machine learning model against the data set based on the class similarity data and the set of robustness criteria, wherein the trained machine learning model enforces multiple robustness criteria, and wherein during training, the outer group loss enforces the strict robustness criterion between different class groups by maximizing a distance between dissimilar classes, while the inner group loss enforces the loose robustness criterion between classes in the same group by minimizing the distance between similar classes; and
using the trained machine learning model for a subsequent classification task.