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.

翻译：生成模型的激增引发了关于视觉内容伪造的严重关切。现有的深度伪造检测方法主要针对图像级分类或像素级定位。尽管某些方法实现了高精度，但它们通常存在跨操纵类型泛化能力有限或依赖复杂架构的问题。本文提出Loupe，一种轻量级但有效的联合深度伪造检测与定位框架。Loupe将块感知分类器与条件查询分割模块相结合，能够同时进行全局真实性分类和细粒度掩码预测。为增强对测试集分布偏移的鲁棒性，Loupe引入伪标签引导的测试时自适应机制，利用块级预测监督分割头。在DDL数据集上的大量实验表明，Loupe实现了最先进的性能，以0.846的总分在IJCAI 2025深度伪造检测与定位挑战赛中位列第一。我们的结果验证了所提出的块级融合和条件查询设计在提升多种伪造模式下的分类精度与空间定位效果方面的有效性。代码发布于 https://github.com/Kamichanw/Loupe。