US 12,229,254 B2
Machine learning fraud resiliency using perceptual descriptors
Raizy Kellermann, Jerusalem (IL); Omer Ben-Shalom, Rishon le-Tzion (IL); and Alex Nayshtut, Gan Yavne (IL)
Assigned to INTEL CORPORATION, Santa Clara, CA (US)
Filed by Intel Corporation, Santa Clara, CA (US)
Filed on Dec. 23, 2021, as Appl. No. 17/560,943.
Prior Publication US 2022/0114255 A1, Apr. 14, 2022
Int. Cl. G06F 21/55 (2013.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01)
CPC G06F 21/554 (2013.01) [G06V 10/7747 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01)] 18 Claims
OG exemplary drawing
 
1. One or more non-transitory computer-readable storage mediums having stored thereon executable computer program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
accessing a plurality of examples in a training dataset for a classifier system;
calculating a plurality of perceptual hashes for each of the plurality of examples, the plurality of perceptual hashes including a plurality of perceptual hash algorithms;
generating clusters of perceptual hashes for the plurality of examples based on the calculation of the plurality of 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 for the inference sample utilizing the generated clusters of perceptual hashes;
comparing the first classification result for the inference sample with the second classification result for the inference sample; and
upon a determination that the first classification result does not match the second classification result, determining a suspicion of an adversarial attack on the classifier system.