Name
Abstract | ||
|---|---|---|
Protecting visual secrets is an important problem due to the prevalence of cameras that continuously monitor our surroundings. Any viable solution to this problem should also minimize the impact on the utility of applications that use images. In this work, we build on the existing work of adversarial learning to design a perturbation mechanism that jointly optimizes privacy and utility objectives. We provide a feasibility study of the proposed mechanism and present ideas on developing a privacy framework based on the adversarial perturbation mechanism. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPRW.2017.174 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | Volume |
Computer vision,Computer security,Visualization,Computer science,Human–computer interaction,Artificial intelligence,Adversarial system | Conference | 2017 |
Issue | ISSN | Citations |
1 | 2160-7508 | 7 |
PageRank | References | Authors |
0.48 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nisarg Raval | 1 | 68 | 5.85 |
| Ashwin Machanavajjhala | 2 | 2624 | 132.52 |
| Landon P. Cox | 3 | 1396 | 109.41 |