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1.	Rosberg, Felix, et al. (author) FIVA : Facial Image and Video Anonymization and Anonymization Defense 2023 In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). - Los Alamitos, CA : IEEE. - 9798350307443 ; , s. 362-371 Conference paper (peer-reviewed)abstract 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.

Rosberg, Felix, et al. (author)
FIVA : Facial Image and Video Anonymization and Anonymization Defense
2023
In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). - Los Alamitos, CA : IEEE. - 9798350307443 ; , s. 362-371
Conference paper (peer-reviewed)abstract
- 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.

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