轻量级框架同时检测与定位图像伪造，性能领先。
Loupe: A Generalizable and Adaptive Framework for Image Forgery Detection
- 采用分块感知分类与条件查询分割模块，联合完成真假判断和区域定位。
- 在DDL数据集上取得0.846的综合得分，位居IJCAI 2025挑战赛第一。
- 引入伪标签自适应机制，提升对未知伪造类型的泛化能力。
生成模型的普及引发了视觉内容伪造的严重担忧。现有深度伪造检测方法主要针对图像级分类或像素级定位，虽部分方法精度高，但泛化能力有限或依赖复杂架构。本文提出Loupe，一种轻量高效、可泛化且自适应的联合检测与定位框架。该框架结合分块感知分类器与带条件查询的分割模块，实现全局真伪分类与细粒度掩码预测同步进行。为增强对测试集分布偏移的鲁棒性，引入基于分块预测的伪标签引导测试时自适应机制，以监督分割头。在DDL数据集上的大量实验表明，Loupe达到当前最优性能，在IJCAI 2025深度伪造检测与定位挑战赛中以0.846的综合得分排名第一。结果验证了分块融合与条件查询设计在多种伪造模式下对分类准确率与空间定位精度的有效提升。代码已开源：https://github.com/Kamichanw/Loupe。
原文摘要 · Abstract (English)
The proliferation of generative models has raised serious concerns about visual content forgery. Existing deepfake detection methods primarily target either image-level classification or pixel-wise localization. While some achieve high accuracy, they often suffer from limited generalization across manipulation types or rely on complex architectures. In this paper, we propose Loupe, a lightweight yet effective framework for joint deepfake detection and localization. Loupe integrates a patch-aware classifier and a segmentation module with conditional queries, allowing simultaneous global authenticity classification and fine-grained mask prediction. To enhance robustness against distribution shifts of test set, Loupe introduces a pseudo-label-guided test-time adaptation mechanism by leveraging patch-level predictions to supervise the segmentation head. Extensive experiments on the DDL dataset demonstrate that Loupe achieves state-of-the-art performance, securing the first place in the IJCAI 2025 Deepfake Detection and Localization Challenge with an overall score of 0.846. Our results validate the effectiveness of the proposed patch-level fusion and conditional query design in improving both classification accuracy and spatial localization under diverse forgery patterns. The code is available at https://github.com/Kamichanw/Loupe.
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