Method and machine learning system for detecting adversarial examples

• Main Authors: Doliwa, Peter, van Vredendaal, Christine, Ermans, Brian
• Format: Patent
• Language: English
• Published: 15.11.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

A method and machine learning system for detecting adversarial examples is provided. A first machine learning model is trained with a first machine learning training data set having only training data samples with robust features. A second machine learning model is trained with a second machine learning training data set, the second machine learning training data set having only training data samples with non-robust features. A feature is a distinguishing element in a data sample. A robust feature is more resistant to adversarial perturbations than a non-robust feature. A data sample is provided to each of the first and second trained machine learning models during an inference operation. if the first trained machine learning model classifies the data sample with high confidence, and the second trained machine learning model classifies the data sample differently with a high confidence, then the data sample is determined to be an adversarial example.