发表机构
Erciyes University; Abdullah Gül University（埃尔西耶斯大学; 阿卜杜拉居尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
BG-REAL是针对背景篡改检测与定位的公共真实数据锚定基准，基于Open Images V7构建，含7000个样本，覆盖多类编辑与基准，可评估模型对重新编码伪影的误判风险，为通用图像篡改定位基准提供补充。
AI 中文摘要
背景篡改是一种实用但规范不足的图像取证场景：篡改证据可能位于显著前景对象之外，而许多评估却聚焦于以对象为中心的复制-移动、拼接或通用合成编辑。我们推出BG-REAL，这是一个用于背景篡改检测与定位的公共真实数据锚定基准包。当前版本基于Open Images V7实例分割源构建，包含1200个源组的7000个处理样本，其中包括6000个公共数据锚定样本和1000个合成对照样本。BG-REAL涵盖6类编辑、匹配的真实对照、源组划分、掩码与泄露质量保证、599个人工辅助质量控制条目、3个已完成的外部基准（TruFor、MVSS-Net和HiFi-Net）以及5种子模型评估。除了总体准确率，我们还使用匹配真实对照诊断来测量基准在保留验证数据固定阈值下，将重新编码的真实图像误判为篡改图像的频率；假阳性率范围从0.57（最低值，TruFor）到1.00（多个弱或掩码感知基准），表明重新编码伪影是所有基准共有的捷径风险，而非特定模型的问题。该版本提供构建流程、评估协议、可直接用于论文的图表以及复现文档。我们将BG-REAL视为通用图像篡改定位基准的背景篡改聚焦补充，而非完全真实或通用的基准。
英文摘要
Background manipulation is a practical but under-specified image-forensics setting: the manipulated evidence can sit outside the salient foreground object, while many evaluations emphasize object-centric copy-move, splicing, or generic synthetic edits. We introduce BG-REAL, a public real-data anchored benchmark package for background manipulation detection and localization. The current release is built from Open Images V7 instance-segmentation sources and contains 7,000 processed samples over 1,200 source groups, including 6,000 public-data anchored samples and 1,000 synthetic control samples. BG-REAL covers six edit families, matched authentic controls, source-group splits, mask and leakage QA, 599 human-assisted quality-control rows, three completed external baselines (TruFor, MVSS-Net, and HiFi-Net), and five-seed model evaluation. Beyond aggregate accuracy, we use matched-authentic-control diagnostics to measure how often baselines misclassify re-encoded authentic images as manipulated at a threshold fixed on held-out validation data; false-positive rates range from 0.57 (TruFor, the lowest) to 1.00 (several weak or mask-informed baselines), indicating that re-encoding artifacts are a shared shortcut risk across baselines rather than a problem specific to any one model. The release provides the construction pipeline, evaluation protocol, paper-ready figures, and reproduction documentation. We frame BG-REAL as a background-manipulation-focused complement to general image-manipulation-localization benchmarks, not as a fully real-only or general-purpose benchmark.
Comments24 pages, 9 figures, 8 tables
