{"url":"/dataset/apricot-mask","name":"APRICOT-Mask","full_name":null,"description_markdown":"We present the APRICOT-Mask dataset, which augments the APRICOT dataset with pixel-level annotations of adversarial patches. We hope APRICOT-Mask along with the APRICOT dataset can facilitate the research in building defenses against physical patch attacks, especially patch detection and removal techniques.","description_withheld":null,"homepage":"https://aiem.jhu.edu/datasets/apricot-mask/","introduced_date":"2021-12-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/segment-and-complete-defending-object","title":"Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection","first_author":"Jiang Liu","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["APRICOT-Mask"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}