构建真实用户编辑需求数据集，推动图像编辑模型落地应用
REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations
- 从Reddit收集真实用户编辑请求与人工修改图像，构建大规模真实数据集
- 新模型在人类评估中比竞品高165 Elo点，自动评分提升92%
- 数据集可用于检测图像篡改，帮助提升深伪识别模型性能
现有图像编辑模型虽在学术评测中表现优异，但难以满足真实用户需求。当前数据集多采用人工构造的编辑操作，缺乏规模与生态真实性。本文提出REALEDIT，一个基于Reddit的真实用户编辑请求与人工修改图像的大规模数据集，包含9300个测试样本，用于评估模型在真实场景下的表现。实验表明，现有模型在此类任务上表现不佳，凸显真实训练数据的重要性。为此，我们构建了48,000条训练样本并训练出REALEDIT模型，在人类判断中领先对手最高达165 Elo点，自动化指标VIEScore相对提升92%。将该模型部署于Reddit后获得积极反馈。此外，我们与非营利深伪检测组织合作，用REALEDIT微调其模型，使F1-score提升14个百分点，证明该数据集在图像真实性检测等领域的广泛价值。
原文摘要 · Abstract (English)
Existing image editing models struggle to meet real-world demands. Despite excelling in academic benchmarks, they have yet to be widely adopted for real user needs. Datasets that power these models use artificial edits, lacking the scale and ecological validity necessary to address the true diversity of user requests. We introduce REALEDIT, a large-scale image editing dataset with authentic user requests and human-made edits sourced from Reddit. REALEDIT includes a test set of 9300 examples to evaluate models on real user requests. Our results show that existing models fall short on these tasks, highlighting the need for realistic training data. To address this, we introduce 48K training examples and train our REALEDIT model, achieving substantial gains - outperforming competitors by up to 165 Elo points in human judgment and 92 percent relative improvement on the automated VIEScore metric. We deploy our model on Reddit, testing it on new requests, and receive positive feedback. Beyond image editing, we explore REALEDIT's potential in detecting edited images by partnering with a deepfake detection non-profit. Finetuning their model on REALEDIT data improves its F1-score by 14 percentage points, underscoring the dataset's value for broad applications.
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