AI 中文总结
GIFTBench是一个包含115,013张篡改图像的多轴基准，用于诊断图像伪造定位的泛化性，并指导开发了ForenScope框架，显著提升跨数据集定位性能。
AI 中文摘要
图像伪造定位（IFL）的可靠评估要求模型在各种分布变化下进行测试，然而现有基准往往覆盖有限的篡改条件，或在跨数据集评估中纠缠多个因素。因此，总体性能无法全面反映定位泛化能力。我们提出了GIFTBench，一个多轴基准，包含115,013张带像素级标注的篡改图像，涵盖篡改来源、语义目标、编辑操作和合成复杂度。GIFTBench支持特定轴的迁移分析和在十二个外部数据集上的评估。其诊断研究揭示了不对称的跨来源迁移、以召回率主导的失败，以及语义、操作和合成变化下的异质退化。除诊断外，GIFTBench的规模和多样性提供了比传统IFL数据集更广泛的训练分布。在GIFTBench上训练代表性定位器，持续提升其对外部数据集的整体迁移性能，表明该基准不仅作为评估工具，也作为跨域定位的有效训练资源。在诊断发现的指导下，我们进一步开发了ForenScope，一个检测与定位框架，结合了分类适配表示与多深度、多尺度空间特征、学习层融合和选择性粗尺度条件。实验表明，在保持图像级检测能力的同时，跨数据集定位性能得到提升。GIFTBench数据集展示页面可在https://this https URL获取。
英文摘要
Reliable evaluation of image forgery localization (IFL) requires assessing models under diverse distribution changes, yet existing benchmarks often cover limited manipulation conditions or entangle multiple factors in cross-dataset evaluation. Consequently, aggregate performance provides an incomplete view of localization generalization. We introduce GIFTBench, a multi-axis benchmark of 115,013 manipulated images with pixel-level annotations spanning manipulation source, semantic target, editing operation, and composition complexity. GIFTBench supports axis-specific transfer analysis and evaluation on twelve external datasets. Its diagnostic studies reveal asymmetric cross-source transfer, recall-dominated failures, and heterogeneous degradation across semantic, operational, and compositional changes. Beyond diagnosis, the scale and diversity of GIFTBench provide a substantially broader training distribution than conventional IFL datasets. Training representative localizers on GIFTBench consistently improves their aggregate transfer to external datasets, showing that the benchmark serves not only as an evaluation tool but also as an effective training resource for cross-domain localization. Guided by the diagnostic findings, we further develop ForenScope, a detection and localization framework combining classification-adapted representations with multi-depth, multi-scale spatial features, learned layer fusion, and selective coarse-scale conditioning. Experiments show improved cross-dataset localization while retaining image-level detection capability. The GIFTBench dataset showcase page is available at https://giftbench-preview.doudoudouya337.chatgpt.site.
