Synthetic and natural face identity processing share common mechanisms
Recent developments in generative AI offer the means to create synthetic identities, or deepfakes, at scale. As deepfake faces and voices become indistinguishable from real ones, they are considered as promising alternatives for research and development to enhance fairness and protect humans' r...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Computers in Human Behavior Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2451958824001969 |
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| author | Kim Uittenhove Hatef Otroshi Shahreza Sébastien Marcel Meike Ramon |
| author_facet | Kim Uittenhove Hatef Otroshi Shahreza Sébastien Marcel Meike Ramon |
| author_sort | Kim Uittenhove |
| collection | DOAJ |
| description | Recent developments in generative AI offer the means to create synthetic identities, or deepfakes, at scale. As deepfake faces and voices become indistinguishable from real ones, they are considered as promising alternatives for research and development to enhance fairness and protect humans' rights to privacy. Notwithstanding these efforts and intentions, a basic question remains unanswered: Are natural faces and facial deepfakes perceived and remembered in the same way? Using images created via professional photography on the one hand, and a state-of-the-art generative model on the other, we investigated the most studied process of face cognition: perceptual matching and discrimination of facial identity. Our results demonstrate that identity discrimination of natural and synthetic faces is governed by the same underlying perceptual mechanisms: objective stimulus similarity and observers’ ability level. These findings provide empirical support both for the societal risks associated with deepfakes, while also underscoring the utility of synthetic identities for research and development. |
| format | Article |
| id | doaj-art-513867cbcf9a434ba7b8e9d2ee97e37a |
| institution | DOAJ |
| issn | 2451-9588 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers in Human Behavior Reports |
| spelling | doaj-art-513867cbcf9a434ba7b8e9d2ee97e37a2025-08-20T02:54:58ZengElsevierComputers in Human Behavior Reports2451-95882025-03-011710056310.1016/j.chbr.2024.100563Synthetic and natural face identity processing share common mechanismsKim Uittenhove0Hatef Otroshi Shahreza1Sébastien Marcel2Meike Ramon3Center for Learning Science, EPFL, Lausanne, Switzerland; Applied Face Cognition Lab, Institute of Psychology, University of Lausanne, SwitzerlandIdiap Research Institute, Martigny, Switzerland; School of Engineering, EPFL, Lausanne, SwitzerlandIdiap Research Institute, Martigny, Switzerland; School of Criminal Justice, University of Lausanne, SwitzerlandApplied Face Cognition Lab, Institute of Psychology, University of Lausanne, Switzerland; AIR – Association for Independent Research, Zurich, Switzerland; Corresponding author. Applied Face Cognition Lab, Institute of Psychology, University of Lausanne, Switzerland.Recent developments in generative AI offer the means to create synthetic identities, or deepfakes, at scale. As deepfake faces and voices become indistinguishable from real ones, they are considered as promising alternatives for research and development to enhance fairness and protect humans' rights to privacy. Notwithstanding these efforts and intentions, a basic question remains unanswered: Are natural faces and facial deepfakes perceived and remembered in the same way? Using images created via professional photography on the one hand, and a state-of-the-art generative model on the other, we investigated the most studied process of face cognition: perceptual matching and discrimination of facial identity. Our results demonstrate that identity discrimination of natural and synthetic faces is governed by the same underlying perceptual mechanisms: objective stimulus similarity and observers’ ability level. These findings provide empirical support both for the societal risks associated with deepfakes, while also underscoring the utility of synthetic identities for research and development.http://www.sciencedirect.com/science/article/pii/S2451958824001969Face identity processingNatural and synthetic facesDeepfakesStimulus similarityHuman abilityIndividual differences |
| spellingShingle | Kim Uittenhove Hatef Otroshi Shahreza Sébastien Marcel Meike Ramon Synthetic and natural face identity processing share common mechanisms Computers in Human Behavior Reports Face identity processing Natural and synthetic faces Deepfakes Stimulus similarity Human ability Individual differences |
| title | Synthetic and natural face identity processing share common mechanisms |
| title_full | Synthetic and natural face identity processing share common mechanisms |
| title_fullStr | Synthetic and natural face identity processing share common mechanisms |
| title_full_unstemmed | Synthetic and natural face identity processing share common mechanisms |
| title_short | Synthetic and natural face identity processing share common mechanisms |
| title_sort | synthetic and natural face identity processing share common mechanisms |
| topic | Face identity processing Natural and synthetic faces Deepfakes Stimulus similarity Human ability Individual differences |
| url | http://www.sciencedirect.com/science/article/pii/S2451958824001969 |
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