Adversarially robust visual fingerprinting and image provenance models
US12183056B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 11, 2022 |
| Grant date | Dec 31, 2024 |
| Priority date | — |
| Expiry date | Jul 30, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0475
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a deep visual fingerprinting model with parameters learned from robust contrastive learning to identify matching digital images and image provenance information. For example, the disclosed systems utilize an efficient learning procedure that leverages training on bounded adversarial examples to more accurately identify digital images (including adversarial images) with a small computational overhead. To illustrate, the disclosed systems utilize a first objective function that iteratively identifies augmentations to increase contrastive loss. Moreover, the disclosed systems utilize a second objective function that iteratively learns parameters of a deep visual fingerprinting model to reduce the contrastive loss. With these learned parameters, the disclosed systems utilize the deep visual fingerprinting model to generate visual fingerprints for digital images, retrieve and match digital images, and provide digital image provenance information.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.