{"url":"/dataset/coverage","name":"COVERAGE","full_name":"Copy-Move Forgery Database with Similar but Genuine Objects","description_markdown":"COVERAGE contains copymove forged (CMFD) images and their originals with similar but genuine objects (SGOs). COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images. In COVERAGE, forged–original pairs are annotated with (i) the duplicated and forged region masks, and (ii) the tampering factor/similarity metric. For benchmarking, forgery quality is evaluated using (i) computer vision-based methods, and (ii) human detection performance.\r\n\r\nSource: [COVERAGE](https://github.com/wenbihan/coverage)","description_withheld":null,"homepage":"https://github.com/wenbihan/coverage","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Image Manipulation Detection","url":"/task/image-manipulation-detection","datasets_with_task":"/datasets/task/image-manipulation-detection"},{"name":"Image Manipulation Localization","url":"/task/image-manipulation-localization","datasets_with_task":"/datasets/task/image-manipulation-localization"}],"languages":[],"variants":["COVERAGE"],"data_loaders":[{"repo":"https://github.com/wenbihan/coverage","url":"https://github.com/wenbihan/coverage","frameworks":[]}],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-manipulation-localization-on-coverage","task":"Image Manipulation Localization","dataset_variant":"COVERAGE","rows":11,"metrics":["Average Pixel F1(Fixed threshold)"],"first_row_in_archive_order":{"model":"Early Fusion","paper":"/paper/exploring-multi-modal-fusion-for-image","metrics":{"Average Pixel F1(Fixed threshold)":".663"},"code_links":[{"title":"idt-iti/mmfusion-iml","url":"https://github.com/idt-iti/mmfusion-iml"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-manipulation-detection-on-coverage","task":"Image Manipulation Detection","dataset_variant":"COVERAGE","rows":8,"metrics":["Balanced Accuracy","AUC"],"first_row_in_archive_order":{"model":"Early Fusion","paper":"/paper/exploring-multi-modal-fusion-for-image","metrics":{"AUC":".839","Balanced Accuracy":".770"},"code_links":[{"title":"idt-iti/mmfusion-iml","url":"https://github.com/idt-iti/mmfusion-iml"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/exploring-multi-modal-fusion-for-image","title":"MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization","date":"2023-12-04","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/trufor-leveraging-all-round-clues-for","title":"TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization","date":"2022-12-21","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/cmx-cross-modal-fusion-for-rgb-x-semantic","title":"CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers","date":"2022-03-09","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-jpeg-compression-artifacts-for-image","title":"Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization","date":"2021-08-30","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/image-manipulation-detection-by-multi-view","title":"Image Manipulation Detection by Multi-View Multi-Scale Supervision","date":"2021-04-14","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":9,"samples_unverified":4,"pointer_only_for_licence":13,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/span-spatial-pyramid-attention-network-for","title":"SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization","date":"2020-08-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/constrained-r-cnn-a-general-image","title":"Constrained R-CNN: A general image manipulation detection model","date":"2019-11-19","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/mantra-net-manipulation-tracing-network-for","title":"ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features","date":"2019-06-01","rows_on_this_dataset":2,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":15,"samples_ran":11,"samples_unverified":4,"pointer_only_for_licence":13,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}