Detecting backdoor attacks using exclusionary reclassification
US11538236B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 16, 2019 |
| Grant date | Dec 27, 2022 |
| Priority date | — |
| Expiry date | Jun 25, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/7753
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments relate to a system, program product, and method for processing an untrusted data set to automatically determine which data points there are poisonous. A neural network is trained network using potentially poisoned training data. Each of the training data points is classified using the network to retain the activations of at least one hidden layer, and segment those activations by the label of corresponding training data. Clustering is applied to the retained activations of each segment, and a clustering assessment is conducted to remove an identified cluster from the data set, form a new training set, and train a second neural model with the new training set. The removed cluster and corresponding data are applied to the trained second neural model to analyze and classify data in the removed cluster as either legitimate or poisonous.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.