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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.23492 |
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| _version_ | 1866914274185576448 |
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| author | Geissler, Dominique Robertson, Claire Feuerriegel, Stefan |
| author_facet | Geissler, Dominique Robertson, Claire Feuerriegel, Stefan |
| contents | Deepfakes images can erode trust in institutions and compromise election outcomes, as people often struggle to discern real images from deepfake images. Improving digital literacy can help address these challenges. Here, we compare the efficacy of five digital literacy interventions to boost people's ability to discern deepfakes: (1) textual guidance on common indicators of deepfakes; (2) visual demonstrations of these indicators; (3) a gamified exercise for identifying deepfakes; (4) implicit learning through repeated exposure and feedback; and (5) explanations of how deepfakes are generated with the help of AI. We conducted an experiment with N=1,200 participants from the United States to test the immediate and long-term effectiveness of our interventions. Our results show that our lightweight, easy-to-understand interventions can boost deepfake image discernment by up to 13 percentage points while maintaining trust in real images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernment Geissler, Dominique Robertson, Claire Feuerriegel, Stefan Human-Computer Interaction Artificial Intelligence Deepfakes images can erode trust in institutions and compromise election outcomes, as people often struggle to discern real images from deepfake images. Improving digital literacy can help address these challenges. Here, we compare the efficacy of five digital literacy interventions to boost people's ability to discern deepfakes: (1) textual guidance on common indicators of deepfakes; (2) visual demonstrations of these indicators; (3) a gamified exercise for identifying deepfakes; (4) implicit learning through repeated exposure and feedback; and (5) explanations of how deepfakes are generated with the help of AI. We conducted an experiment with N=1,200 participants from the United States to test the immediate and long-term effectiveness of our interventions. Our results show that our lightweight, easy-to-understand interventions can boost deepfake image discernment by up to 13 percentage points while maintaining trust in real images. |
| title | Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernment |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2507.23492 |