MACHINE LEARNING FRAUD RESILIENCY USING PERCEPTUAL DESCRIPTORS

• Main Authors: Ben-Shalom, Omer, Nayshtut, Alex, Kellermann, Raizy
• Format: Patent
• Language: English
• Published: 14.04.2022
• Subjects: CALCULATING; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

Machine learning fraud resiliency using perceptual descriptors is described. An example of a computer-readable storage medium includes instructions for accessing multiple examples in a training dataset for a classifier system; calculating one or more perceptual hashes for each of the examples; generating clusters of perceptual hashes for the multiple examples based on the calculation of the one or more perceptual hashes for each of the plurality of examples; obtaining an inference sample for classification by the classifier system; generating a first classification result for the inference sample utilizing a neural network classifier and generating a second classification result utilizing the generated clusters of perceptual hashes; comparing the first classification result with the second classification result; and, upon a determination that the first classification result does not match the second classification result, determining a suspicion of an adversarial attack.