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FIVA :
FIVA : Facial Image and Video Anonymization and Anonymization Defense
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- Rosberg, Felix (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi,Berge Consulting, Gothenburg, Sweden
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- Aksoy, Eren, 1982- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Englund, Cristofer, 1977- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Alonso-Fernandez, Fernando, 1978- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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(creator_code:org_t)
- Los Alamitos, CA : IEEE, 2023
- 2023
- Engelska.
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Ingår i: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). - Los Alamitos, CA : IEEE. - 9798350307443 ; , s. 362-371
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In this paper, we present a new approach for facial anonymization in images and videos, abbreviated as FIVA. Our proposed method is able to maintain the same face anonymization consistently over frames with our suggested identity-tracking and guarantees a strong difference from the original face. FIVA allows for 0 true positives for a false acceptance rate of 0.001. Our work considers the important security issue of reconstruction attacks and investigates adversarial noise, uniform noise, and parameter noise to disrupt reconstruction attacks. In this regard, we apply different defense and protection methods against these privacy threats to demonstrate the scalability of FIVA. On top of this, we also show that reconstruction attack models can be used for detection of deep fakes. Last but not least, we provide experimental results showing how FIVA can even enable face swapping, which is purely trained on a single target image. © 2023 IEEE.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Anonymization
- Deep Fakes
- Facial Recognition
- Identity Tracking
- Reconstruction Attacks
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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