Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911986001903616 |
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| author | Mathys, Melanie Willi, Marco Meier, Raphael |
| author_facet | Mathys, Melanie Willi, Marco Meier, Raphael |
| contents | Artificial Intelligence (AI) tools have become incredibly powerful in generating synthetic images. Of particular concern are generated images that resemble photographs as they aspire to represent real world events. Synthetic photographs may be used maliciously by a broad range of threat actors, from scammers to nation-state actors, to deceive, defraud, and mislead people. Mitigating this threat usually involves answering a basic analytic question: Is the photograph real or synthetic? To address this, we have examined the capabilities of recent generative diffusion models and have focused on their flaws: visible artifacts in generated images which reveal their synthetic origin to the trained eye. We categorize these artifacts, provide examples, discuss the challenges in detecting them, suggest practical applications of our work, and outline future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06398 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI Mathys, Melanie Willi, Marco Meier, Raphael Computers and Society Computer Vision and Pattern Recognition K.4.0; I.2.0; I.4.0 Artificial Intelligence (AI) tools have become incredibly powerful in generating synthetic images. Of particular concern are generated images that resemble photographs as they aspire to represent real world events. Synthetic photographs may be used maliciously by a broad range of threat actors, from scammers to nation-state actors, to deceive, defraud, and mislead people. Mitigating this threat usually involves answering a basic analytic question: Is the photograph real or synthetic? To address this, we have examined the capabilities of recent generative diffusion models and have focused on their flaws: visible artifacts in generated images which reveal their synthetic origin to the trained eye. We categorize these artifacts, provide examples, discuss the challenges in detecting them, suggest practical applications of our work, and outline future research directions. |
| title | Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI |
| topic | Computers and Society Computer Vision and Pattern Recognition K.4.0; I.2.0; I.4.0 |
| url | https://arxiv.org/abs/2408.06398 |