Systems and methods for increasing robustness of machine-learned models and other software systems against adversarial attacks
US11263323B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 30, 2019 |
| Grant date | Mar 1, 2022 |
| Priority date | — |
| Expiry date | Aug 20, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2221/033
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure provides systems and methods that reduce vulnerability of software systems (e.g., machine-learned models) to adversarial attacks by increasing variety within the software system. In particular, a software system can include a number of subcomponents that interoperate using predefined interfaces. To increase variety within the software system, multiple, different versions of one or more of the subcomponents of the software system can be generated. In particular, the different versions of the subcomponent(s) can be different from each other in some way, while still remaining functionally equivalent (e.g., able to perform the same functions with comparable accuracy/success). A plurality of different variants of the software system can be constructed by mixing and matching different versions of the subcomponents. A large amount of variety can be exhibited by the variants of the software system deployed at a given time, thereby leading to increased robustness against adversarial attacks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.