{"url":"/dataset/trojans-against-trojans-tat","name":"Trojans Against Trojans (TAT)","full_name":"Trojans Against Trojans","description_markdown":"The dataset contains 1,200 trained ViT-B-16 models, trained on ImageNet. Half of the models are benign. The other half constitutes Trojan models, each trained with a randomly generated trigger that makes the model predict a specific target class, chosen at random for each Trojan model.","description_withheld":null,"homepage":"https://github.com/vimal-isi-edu/tat","introduced_date":"2023-12-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/trojan-model-detection-using-activation","title":"TRIGS: Trojan Identification from Gradient-based Signatures","first_author":"Mohamed E. Hussein","url":null},"license":{"name":"Custom","url":"https://github.com/vimal-isi-edu/tat/tree/main?tab=License-1-ov-file"},"modalities":[],"tasks":[{"name":"backdoor defense","url":"/task/backdoor-defense","datasets_with_task":"/datasets/task/backdoor-defense"}],"languages":[],"variants":["Trojans Against Trojans (TAT)"],"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."}