LayLens: Improving Deepfake Understanding through Simplified Explanations

TL;DR

LayLens is a tool that simplifies deepfake explanations through a three-stage pipeline, making deepfake detection more understandable and accessible for users of all backgrounds.

Contribution

It introduces a novel pipeline combining explainable detection, language simplification, and image reconstruction to enhance user understanding of deepfakes.

Findings

Simplified explanations improve user clarity.

Users report increased confidence in deepfake identification.

The tool demonstrates effective bridging of technical and layperson understanding.

Abstract

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.