公开(公告)号：US20220141251A1

公开(公告)日：2022-05-05

申请号：US17083928

申请日：2020-10-29

Applicant: Booz Allen Hamilton Inc.

Inventor： Luke Edward RICHARDS ,  Andre Tai NGUYEN ,  Ryan Joseph CAPPS ,  Edward Simon Paster RAFF

IPC: H04L29/06 ,  G06K9/62 ,  G06N20/00

Abstract: A system and method for transferring an adversarial attack involving generating a surrogate model having an architecture and a dataset that mirrors at least one aspect of a target model of a target module, wherein the surrogate model includes a plurality of classes. The method involves generating a masked version of the surrogate model having ewer classes than the surrogate model by randomly selecting at least one class of the plurality of classes for removal. The method involves attacking the masked surrogate model to create a perturbed sample. The method involves generalizing the perturbed sample for use with the target module. The method involves transferring the perturbed sample to the target module to alter an operating parameter of the target model.

公开(公告)号：US20210406309A1

公开(公告)日：2021-12-30

申请号：US17343474

申请日：2021-06-09

Applicant: Booz Allen Hamilton Inc.

Inventor： Andre Tai NGUYEN ,  Luke Edward RICHARDS ,  Edward Simon Paster RAFF

IPC: G06F16/906 ,  G06K9/62

Abstract: A method and system for cross-modal manifold alignment of different data domains includes determining for a shared embedding space a first embedding function for data of a first domain and a second embedding function for data of a second domain using a triplet loss, wherein triplets of the triplet loss include an anchor data point from the first, a positive and a negative data point from the second domain; creating a first mapping for the data of the first domain using the first embedding function in the shared embedding space; creating a second mapping for the data of the second domain using the second embedding function in the shared embedding space; and generating a cross-modal alignment for the data of the first domain and the data of the second domain.