Detecting possible attacks on artificial intelligence models using strengths of causal relationships in training data
US12361174B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 31, 2023 |
| Grant date | Jul 15, 2025 |
| Priority date | — |
| Expiry date | Apr 2, 2044 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/1425
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for managing artificial intelligence (AI) models are disclosed. To manage AI models, an instance of an AI model may not be re-trained using training data determined to be potentially poisoned. By doing so, malicious attacks intending to influence the AI model using poisoned training data may be prevented. To do so, a first level of strength of a first causal relationship present in historical training data may be compared to a second level of strength of a second causal relationship present in a candidate training data set. The first level of strength and the second level of strength may be expected to be similar within a threshold. If a difference between the first level of strength and the second level of strength is not within the threshold, the candidate training data may be treated as including poisoned training data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.