用通俗语言解释深度伪造检测，让普通人也能看懂真假。
LayLens: Improving Deepfake Understanding through Simplified Explanations
- 三阶段流程：检测伪造、简化技术解释、重建原始图像
- 用户研究显示简化说明显著提升理解力与判断信心
- 适合普通用户、教育背景有限者使用，推动可信鉴伪
本演示论文介绍LayLens，一款旨在降低深度伪造理解门槛的工具。现有方法常依赖技术术语，而LayLens通过三阶段流程弥合模型推理与人类理解之间的差距：(1) 使用先进的伪造定位模型进行可解释的深度伪造检测；(2) 利用视觉-语言模型将技术解释转化为自然语言；(3) 通过引导式图像编辑重建可能的原始图像。界面同时提供技术性与通俗化解释，并支持上传图像与重建结果的并列对比。对15名参与者进行的用户研究表明，简化解释显著提升了清晰度并降低了认知负荷，多数用户表示对识别深度伪造更有信心。LayLens为实现透明、可信且以用户为中心的深度伪造取证迈出了重要一步。
原文摘要 · Abstract (English)
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.
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