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Adversarial Robustness datasets
archive 2025-07-28
13 datasets carry the task tag "Adversarial Robustness" (the task itself: Adversarial Robustness), ordered by the archive's paper count. Page 1 of 1: 13 shown of 13. Facet routes are this site's own (the archive records the tag string, not a page).
The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.
Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets
Adversarial Robustness datasets 1–13 of 13
description withheld: archive row vandalised before snapshot
16,145 papers · 91 benchmarks
The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy.
15,430 papers · 52 benchmarks
The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images.
9,045 papers · 51 benchmarks
ImageNet-C is an open source data set that consists of algorithmically generated corruptions (blur, noise) applied to the ImageNet test-set.
602 papers · 4 benchmarks
The ImageNet-A dataset consists of real-world, unmodified, and naturally occurring examples that are misclassified by ResNet models.
431 papers · 5 benchmarks
The Stylized-ImageNet dataset is created by removing local texture cues in ImageNet while retaining global shape information on natural images via AdaIN style transfer.
106 papers · 1 benchmark
Adversarial GLUE (AdvGLUE) is a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks.
36 papers · 1 benchmark
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it.
5 papers · 0 benchmarks
CLEAR-Bias (Corpus for Linguistic Evaluation of Adversarial Robustness against Bias)
CLEAR-Bias is a benchmark dataset designed to evaluate the robustness of large language models (LLMs) against bias elicitation, particularly under adversarial conditions.
2 papers · 0 benchmarks
AdvSuffixes - Information AdvSuffixes is a curated dataset of adversarial prompts and suffixes designed to evaluate and enhance the robustness of large language models (LLMs) against adversarial attacks.
1 paper · 0 benchmarks
This is the paper “DF-RAP: A Robust Adversarial Perturbation for Defending against Deepfakes in Real-world Social Network Scenarios" OSN-transmission CelebA sampling dataset collected by manual upload and download.
1 paper · 0 benchmarks
SHADR (sythetic SDoH Human Annotated Demographic Robustness dataset (SHADR))
SDoH Human Annotated Demoographic Robustness (SHADR) Dataset Overview The Social determinants of health (SDoH) play a pivotal role in determining patient outcomes.
1 paper · 0 benchmarks
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
1 paper · 0 benchmarks
Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.