Bayesian-optimization-based query-efficient black-box adversarial attacks
US11494639B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 24, 2019 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | Jun 20, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Performing an adversarial attack on a neural network classifier is described. A dataset of input-output pairs is constructed, each input element of the input-output pairs randomly chosen from a search space, each output element of the input-output pairs indicating a prediction output of the neural network classifier for the corresponding input element. A Gaussian process is utilized on the dataset of input-output pairs to optimize an acquisition function to find a best perturbation input element from the dataset. The best perturbation input element is upsampled to generate an upsampled best input element. The upsampled best input element is added to an original input to generate a candidate input. The neural network classifier is queried to determine a classifier prediction for the candidate input. A score for the classifier prediction is computed. The candidate input is accepted as a successful adversarial attack responsive to the classifier prediction being incorrect.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.