LayLens: Improving Deepfake Understanding through Simplified Explanations
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913986191032320 |
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| author | Narang, Abhijeet Gupta, Parul Su, Liuyijia Dhall, Abhinav |
| author_facet | Narang, Abhijeet Gupta, Parul Su, Liuyijia Dhall, Abhinav |
| contents | This demonstration paper presents $\mathbf{LayLens}$, a tool aimed to make deepfake understanding easier for users of all educational backgrounds. While prior works often rely on outputs containing technical jargon, LayLens bridges the gap between model reasoning and human understanding through a three-stage pipeline: (1) explainable deepfake detection using a state-of-the-art forgery localization model, (2) natural language simplification of technical explanations using a vision-language model, and (3) visual reconstruction of a plausible original image via guided image editing. The interface presents both technical and layperson-friendly explanations in addition to a side-by-side comparison of the uploaded and reconstructed images. A user study with 15 participants shows that simplified explanations significantly improve clarity and reduce cognitive load, with most users expressing increased confidence in identifying deepfakes. LayLens offers a step toward transparent, trustworthy, and user-centric deepfake forensics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LayLens: Improving Deepfake Understanding through Simplified Explanations Narang, Abhijeet Gupta, Parul Su, Liuyijia Dhall, Abhinav Multimedia Computer Vision and Pattern Recognition This demonstration paper presents $\mathbf{LayLens}$, a tool aimed to make deepfake understanding easier for users of all educational backgrounds. While prior works often rely on outputs containing technical jargon, LayLens bridges the gap between model reasoning and human understanding through a three-stage pipeline: (1) explainable deepfake detection using a state-of-the-art forgery localization model, (2) natural language simplification of technical explanations using a vision-language model, and (3) visual reconstruction of a plausible original image via guided image editing. The interface presents both technical and layperson-friendly explanations in addition to a side-by-side comparison of the uploaded and reconstructed images. A user study with 15 participants shows that simplified explanations significantly improve clarity and reduce cognitive load, with most users expressing increased confidence in identifying deepfakes. LayLens offers a step toward transparent, trustworthy, and user-centric deepfake forensics. |
| title | LayLens: Improving Deepfake Understanding through Simplified Explanations |
| topic | Multimedia Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.10066 |