{"url":"/dataset/reap","name":"REAP","full_name":null,"description_markdown":"**REAP** is a digital benchmark that allows the user to evaluate patch attacks on real images, and under real-world conditions. Built on top of the Mapillary Vistas dataset, the benchmark contains over 14,000 traffic signs. Each sign is augmented with a pair of geometric and lighting transformations, which can be used to apply a digitally generated patch realistically onto the sign.\r\n\r\nSource: [REAP: A Large-Scale Realistic Adversarial Patch Benchmark](https://arxiv.org/pdf/2212.05680v1.pdf)\r\n\r\nImage Source: [https://arxiv.org/pdf/2212.05680v1.pdf](https://arxiv.org/pdf/2212.05680v1.pdf)","description_withheld":null,"homepage":"https://github.com/wagner-group/reap-benchmark","introduced_date":"2022-12-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/reap-a-large-scale-realistic-adversarial","title":"REAP: A Large-Scale Realistic Adversarial Patch Benchmark","first_author":"Nabeel Hingun","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Adversarial Attack","url":"/task/adversarial-attack","datasets_with_task":"/datasets/task/adversarial-attack"}],"languages":[],"variants":["REAP"],"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":"2026-09-24T18:15:14+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."}