Machine learning model adversarial attack monitoring
US12026255B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 14, 2024 |
| Grant date | Jul 2, 2024 |
| Priority date | — |
| Expiry date | Feb 14, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2221/034
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Adversarial attacks on a machine learning model are detected by receiving vectorized data input into the machine learning model along with outputs of the machine learning model responsive to the vectorized data. The vectorized data corresponds to a plurality of queries of the machine learning model by a requesting user. A confidence level is determined which characterizes a likelihood of the vectorized data being part of a malicious act directed to the machine learning model by the requesting user. Data providing the determined confidence levels can be provided to a consuming application or process. Multi-tenant architectures are also provided in which multiple machine learning models associated with different customers can be centrally monitored.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.