BG-REAL: A Public Real-Data Anchored Benchmark for Background Manipulation Detection and Localization
BG-REAL is a benchmark focusing on background manipulation detection and localization, built from Open Images V7 with 7,000 samples covering six edit families and matched authentic controls. Evaluation reveals that re-encoding artifacts are a shared shortcut risk across baselines, with false positive rates ranging from 0.57 to 1.00.
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[Submitted on 28 Jul 2026]
Title:BG-REAL: A Public Real-Data Anchored Benchmark for Background Manipulation Detection and Localization
View a PDF of the paper titled BG-REAL: A Public Real-Data Anchored Benchmark for Background Manipulation Detection and Localization, by Bugra Alperen Uluirmak and 1 other authors
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Abstract: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.
Comments: 24 pages, 9 figures, 8 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.26232 [cs.CV]
(or arXiv:2607.26232v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.26232
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Buğra Alperen Uluırmak [view email] [v1] Tue, 28 Jul 2026 20:11:26 UTC (2,601 KB)
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