Development of a Privacy-Preserving UAV System With Deep Learning-Based Face Anonymization
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Harim | - |
dc.contributor.author | Kim, Myeung Un | - |
dc.contributor.author | Kim, Yeongjun | - |
dc.contributor.author | Lyu, Hyeonsu | - |
dc.contributor.author | Yang, Hyun Jong | - |
dc.date.accessioned | 2021-10-13T00:40:18Z | - |
dc.date.available | 2021-10-13T00:40:18Z | - |
dc.date.created | 2021-10-13 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20017 | - |
dc.description.abstract | In this paper, we develop a privacy-preserving UAV system that does not infringe on the privacy of people in the videos taken by UAVs. Instead of blurring or masking the face parts of the videos, we want to exquisitely modify only the face parts so that the people in the modified videos still look like humans, but they become anonymous. Doing so, the semantic information of the videos can be preserved even with the anonymization. Specifically, based on the latest generative adversarial network architecture, we propose a deep learning-based face-anonymization scheme so that each modified face part looks like the face of a person who does not actually exist. The trained face-anonymizer is then mounted on the UAV system we have implemented. Through experiments, we confirm that the developed privacy-preserving UAV system anonymizes UAV's first-person videos so that the people in the video are not recognized as anyone in the dataset used. In addition, we show that even with such anonymized videos, the perception performance required for performing UAV's essential functions such as simultaneous localization and mapping is not degraded. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Development of a Privacy-Preserving UAV System With Deep Learning-Based Face Anonymization | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Harim | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3113186 | - |
dc.identifier.wosid | 000702544200001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.132652 - 132662 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.citation.startPage | 132652 | - |
dc.citation.endPage | 132662 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | Face recognition | - |
dc.subject.keywordAuthor | Faces | - |
dc.subject.keywordAuthor | Videos | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Privacy | - |
dc.subject.keywordAuthor | Unmanned aerial vehicles | - |
dc.subject.keywordAuthor | Semantics | - |
dc.subject.keywordAuthor | Privacy infringement | - |
dc.subject.keywordAuthor | privacy-preserving vision | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | security robot | - |
dc.subject.keywordAuthor | UAV patrol system | - |
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