arXiv:2607.26232 · v3
Image Forgery Detection under Generative and Composited Background Replacement: A Benchmark with Processing-Matched Controls
摘要
Consumer tools can now keep the subject of a photograph and replace, relight or edit everything behind it. Such edits place genuine people and objects in scenes they were never in, yet involve processing--resampling, harmonization, diffusion autoencoders--that also occurs in benign workflows. We introduce BG-REAL, a benchmark of 25,624 images built from 2,485 Open Images photographs with a dominant subject and real donor scenes. Each recipient is edited by donor compositing, learned harmonization, generative background inpainting, foreground-conditioned relighting and local background edits, and is paired with authentic controls that share the processing but not the semantic edit; all images of a recipient share file format and JPEG quality. Three generators (including FLUX.1 Fill), scene categories and subject classes are held out. Seven detectors published between 2022 and 2026 reach zero-shot AUROCs of at most 0.745 and have no useful operating point: manipulation localisers mark the preserved subject instead of the replaced background, and a detector of AI-generated images flags most authentic images passed through a diffusion autoencoder. After fine-tuning, the strongest detectors reach an AUROC of about 0.95, while re-implemented background-forgery detectors remain below 0.80. Controls show that full-background replacements by the training generators are detected from the edit itself, whereas local edits and SDXL and FLUX edits remain hard, and that full-background localisation largely reduces to subject segmentation. A model-agnostic training objective that uses the controls improves detection on unseen subjects for one detector and on local edits for another without changing in-distribution accuracy. The data are released.
作者备注：22 pages, 2 figures, 8 tables. v2: substantially revised benchmark (new data, edit families, matched controls, held-out splits) and evaluation of 2022-2026 detectors, including an AI-generated-image detector and a FLUX.1 Fill held-out generator. Data: https://huggingface.co/datasets/NoirZangetsu/bg-real