公开(公告)号：US11669642B1

公开(公告)日：2023-06-06

申请号：US17322524

申请日：2021-05-17

Applicant: ARCHITECTURE TECHNOLOGY CORPORATION

Inventor： Daniel Mcardle ,  Judson Powers

IPC: G06F21/52 ,  G06F21/64 ,  G06F8/52

CPC classification number: G06F21/64 ,  G06F8/52 ,  G06F21/52

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise a processor, which provides runtime enforcement of data flow integrity. The processor accesses the application binary file from the disk to execute an application and translates the application binary into intermediate representation. The processor applies the logic of data flow integrity controls to the intermediate representation. Specifically, the processor identifies the vulnerable code in the intermediate representation. The processor applies data flow integrity controls to the vulnerable code. The processor adds simple instrumentation that only changes the application's behavior when unauthorized data tampering occurs while preserving the application's normal behavior. When certain operations may cause unauthorized data tampering, the processor takes proper measures to stop the operations. The processor translates the intermediate representation back to a machine code and replaces the original binary with the machine code.

公开(公告)号：US11869235B1

公开(公告)日：2024-01-09

申请号：US17690312

申请日：2022-03-09

Applicant: ARCHITECTURE TECHNOLOGY CORPORATION

Inventor： Paul Nicotera ,  Robert Joyce ,  Judson Powers ,  Daniel Mcardle

IPC: G06K9/00 ,  G06V20/13 ,  G06V20/10

CPC classification number: G06V20/13 ,  G06T2207/10032 ,  G06T2207/30181 ,  G06T2207/30188 ,  G06V20/176 ,  G06V20/182 ,  G06V20/188

Abstract: Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which provides a terrain segmentation and classification tool for synthetic aperture radar (SAR) imagery. The server accurately segments and classifies terrain types in SAR imagery and automatically adapts to new radar sensors data. The server receives a first SAR imagery and trains an autoencoder based on the first SAR imagery to generate learned representations of the first SAR imagery. The server trains a classifier based on labeled data of the first SAR imagery data to recognize terrain types from the learned representations of the first SAR imagery. The server receives a terrain query for a second SAR imagery. The server translates the second imagery data into the first imagery data and classifies the second SAR imagery terrain types using the classifier trained for the first SAR imagery. By reusing the original classifier, the server improves system efficiency.