{"url":"/dataset/imagenet-patch","name":"ImageNet-Patch","full_name":null,"description_markdown":"ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches\r\n\r\nAdversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding, and requires careful hyperparameter tuning, potentially leading to suboptimal robustness evaluations. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches, optimized to generalize across different models, and readily applicable to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations.","description_withheld":null,"homepage":"https://github.com/pralab/ImageNet-Patch","introduced_date":"2022-03-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/imagenet-patch-a-dataset-for-benchmarking","title":"ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches","first_author":"Maura Pintor","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Adversarial Robustness","url":"/task/adversarial-robustness","datasets_with_task":"/datasets/task/adversarial-robustness"}],"languages":[],"variants":["ImageNet-Patch"],"data_loaders":[{"repo":"https://github.com/pralab/imagenet-patch","url":"https://github.com/pralab/imagenet-patch","frameworks":[]}],"num_papers_in_archive":5,"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."}