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Datasets:
legolasflagstaff
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RobustGenBench

Tasks:
Image Classification
Modalities:
Image
Text
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K - 1M
Tags:
adversarial-robustness
common-corruptions
robustness
benchmark
transfer-attacks
vision-language-models
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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RobustGenBench / README.md
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metadata 
license: cc-by-nc-4.0
language:
  - en
pretty_name: RobustGenBench
size_categories:
  - 100K<n<1M
task_categories:
  - image-classification
task_ids:
  - multi-class-image-classification
tags:
  - adversarial-robustness
  - common-corruptions
  - robustness
  - benchmark
  - transfer-attacks
  - vision-language-models
  - autoattack
configs:
  - config_name: caltech101
    default: true
    data_files:
      - split: train
        path: data/caltech101/train-*.parquet
      - split: validation
        path: data/caltech101/validation-*.parquet
      - split: test
        path: data/caltech101/test-*.parquet
      - split: common
        path: data/caltech101/common-*.parquet
      - split: random_linf_30
        path: data/caltech101/random_linf_30-*.parquet
      - split: clip_vitb16_l1_75
        path: data/caltech101/clip_vitb16_l1_75-*.parquet
      - split: clip_vitb16_l1_300
        path: data/caltech101/clip_vitb16_l1_300-*.parquet
      - split: clip_vitb16_l2_2
        path: data/caltech101/clip_vitb16_l2_2-*.parquet
      - split: clip_vitb16_l2_8
        path: data/caltech101/clip_vitb16_l2_8-*.parquet
      - split: clip_vitb16_linf_4
        path: data/caltech101/clip_vitb16_linf_4-*.parquet
      - split: clip_vitb16_linf_8
        path: data/caltech101/clip_vitb16_linf_8-*.parquet
      - split: clip_vitb16_linf_30
        path: data/caltech101/clip_vitb16_linf_30-*.parquet
      - split: clip_vith14_l1_75
        path: data/caltech101/clip_vith14_l1_75-*.parquet
      - split: clip_vith14_l1_300
        path: data/caltech101/clip_vith14_l1_300-*.parquet
      - split: clip_vith14_l2_2
        path: data/caltech101/clip_vith14_l2_2-*.parquet
      - split: clip_vith14_l2_8
        path: data/caltech101/clip_vith14_l2_8-*.parquet
      - split: clip_vith14_linf_4
        path: data/caltech101/clip_vith14_linf_4-*.parquet
      - split: clip_vith14_linf_8
        path: data/caltech101/clip_vith14_linf_8-*.parquet
      - split: clip_vith14_linf_30
        path: data/caltech101/clip_vith14_linf_30-*.parquet
      - split: metaclip_vith14_l1_75
        path: data/caltech101/metaclip_vith14_l1_75-*.parquet
      - split: metaclip_vith14_l1_300
        path: data/caltech101/metaclip_vith14_l1_300-*.parquet
      - split: metaclip_vith14_l2_2
        path: data/caltech101/metaclip_vith14_l2_2-*.parquet
      - split: metaclip_vith14_l2_8
        path: data/caltech101/metaclip_vith14_l2_8-*.parquet
      - split: metaclip_vith14_linf_4
        path: data/caltech101/metaclip_vith14_linf_4-*.parquet
      - split: metaclip_vith14_linf_8
        path: data/caltech101/metaclip_vith14_linf_8-*.parquet
      - split: metaclip_vith14_linf_30
        path: data/caltech101/metaclip_vith14_linf_30-*.parquet
      - split: siglip2_so400m_l1_75
        path: data/caltech101/siglip2_so400m_l1_75-*.parquet
      - split: siglip2_so400m_l1_300
        path: data/caltech101/siglip2_so400m_l1_300-*.parquet
      - split: siglip2_so400m_l2_2
        path: data/caltech101/siglip2_so400m_l2_2-*.parquet
      - split: siglip2_so400m_l2_8
        path: data/caltech101/siglip2_so400m_l2_8-*.parquet
      - split: siglip2_so400m_linf_4
        path: data/caltech101/siglip2_so400m_linf_4-*.parquet
      - split: siglip2_so400m_linf_8
        path: data/caltech101/siglip2_so400m_linf_8-*.parquet
      - split: siglip2_so400m_linf_30
        path: data/caltech101/siglip2_so400m_linf_30-*.parquet
      - split: siglip2_base_linf_30
        path: data/caltech101/siglip2_base_linf_30-*.parquet
  - config_name: fgvc-aircraft-2013b
    data_files:
      - split: train
        path: data/fgvc-aircraft-2013b/train-*.parquet
      - split: validation
        path: data/fgvc-aircraft-2013b/validation-*.parquet
      - split: test
        path: data/fgvc-aircraft-2013b/test-*.parquet
      - split: common
        path: data/fgvc-aircraft-2013b/common-*.parquet
      - split: random_linf_30
        path: data/fgvc-aircraft-2013b/random_linf_30-*.parquet
      - split: clip_vitb16_l1_75
        path: data/fgvc-aircraft-2013b/clip_vitb16_l1_75-*.parquet
      - split: clip_vitb16_l1_300
        path: data/fgvc-aircraft-2013b/clip_vitb16_l1_300-*.parquet
      - split: clip_vitb16_l2_2
        path: data/fgvc-aircraft-2013b/clip_vitb16_l2_2-*.parquet
      - split: clip_vitb16_l2_8
        path: data/fgvc-aircraft-2013b/clip_vitb16_l2_8-*.parquet
      - split: clip_vitb16_linf_4
        path: data/fgvc-aircraft-2013b/clip_vitb16_linf_4-*.parquet
      - split: clip_vitb16_linf_8
        path: data/fgvc-aircraft-2013b/clip_vitb16_linf_8-*.parquet
      - split: clip_vitb16_linf_30
        path: data/fgvc-aircraft-2013b/clip_vitb16_linf_30-*.parquet
      - split: clip_vith14_l1_75
        path: data/fgvc-aircraft-2013b/clip_vith14_l1_75-*.parquet
      - split: clip_vith14_l1_300
        path: data/fgvc-aircraft-2013b/clip_vith14_l1_300-*.parquet
      - split: clip_vith14_l2_2
        path: data/fgvc-aircraft-2013b/clip_vith14_l2_2-*.parquet
      - split: clip_vith14_l2_8
        path: data/fgvc-aircraft-2013b/clip_vith14_l2_8-*.parquet
      - split: clip_vith14_linf_4
        path: data/fgvc-aircraft-2013b/clip_vith14_linf_4-*.parquet
      - split: clip_vith14_linf_8
        path: data/fgvc-aircraft-2013b/clip_vith14_linf_8-*.parquet
      - split: clip_vith14_linf_30
        path: data/fgvc-aircraft-2013b/clip_vith14_linf_30-*.parquet
      - split: metaclip_vith14_l1_75
        path: data/fgvc-aircraft-2013b/metaclip_vith14_l1_75-*.parquet
      - split: metaclip_vith14_l1_300
        path: data/fgvc-aircraft-2013b/metaclip_vith14_l1_300-*.parquet
      - split: metaclip_vith14_l2_2
        path: data/fgvc-aircraft-2013b/metaclip_vith14_l2_2-*.parquet
      - split: metaclip_vith14_l2_8
        path: data/fgvc-aircraft-2013b/metaclip_vith14_l2_8-*.parquet
      - split: metaclip_vith14_linf_4
        path: data/fgvc-aircraft-2013b/metaclip_vith14_linf_4-*.parquet
      - split: metaclip_vith14_linf_8
        path: data/fgvc-aircraft-2013b/metaclip_vith14_linf_8-*.parquet
      - split: metaclip_vith14_linf_30
        path: data/fgvc-aircraft-2013b/metaclip_vith14_linf_30-*.parquet
      - split: siglip2_base_linf_30
        path: data/fgvc-aircraft-2013b/siglip2_base_linf_30-*.parquet
  - config_name: flowers-102
    data_files:
      - split: train
        path: data/flowers-102/train-*.parquet
      - split: validation
        path: data/flowers-102/validation-*.parquet
      - split: test
        path: data/flowers-102/test-*.parquet
      - split: common
        path: data/flowers-102/common-*.parquet
      - split: random_linf_30
        path: data/flowers-102/random_linf_30-*.parquet
      - split: clip_vitb16_l1_75
        path: data/flowers-102/clip_vitb16_l1_75-*.parquet
      - split: clip_vitb16_l1_300
        path: data/flowers-102/clip_vitb16_l1_300-*.parquet
      - split: clip_vitb16_l2_2
        path: data/flowers-102/clip_vitb16_l2_2-*.parquet
      - split: clip_vitb16_l2_8
        path: data/flowers-102/clip_vitb16_l2_8-*.parquet
      - split: clip_vitb16_linf_4
        path: data/flowers-102/clip_vitb16_linf_4-*.parquet
      - split: clip_vitb16_linf_8
        path: data/flowers-102/clip_vitb16_linf_8-*.parquet
      - split: clip_vitb16_linf_30
        path: data/flowers-102/clip_vitb16_linf_30-*.parquet
      - split: clip_vith14_l1_75
        path: data/flowers-102/clip_vith14_l1_75-*.parquet
      - split: clip_vith14_l1_300
        path: data/flowers-102/clip_vith14_l1_300-*.parquet
      - split: clip_vith14_l2_2
        path: data/flowers-102/clip_vith14_l2_2-*.parquet
      - split: clip_vith14_l2_8
        path: data/flowers-102/clip_vith14_l2_8-*.parquet
      - split: clip_vith14_linf_4
        path: data/flowers-102/clip_vith14_linf_4-*.parquet
      - split: clip_vith14_linf_8
        path: data/flowers-102/clip_vith14_linf_8-*.parquet
      - split: clip_vith14_linf_30
        path: data/flowers-102/clip_vith14_linf_30-*.parquet
      - split: metaclip_vith14_l1_75
        path: data/flowers-102/metaclip_vith14_l1_75-*.parquet
      - split: metaclip_vith14_l1_300
        path: data/flowers-102/metaclip_vith14_l1_300-*.parquet
      - split: metaclip_vith14_l2_2
        path: data/flowers-102/metaclip_vith14_l2_2-*.parquet
      - split: metaclip_vith14_l2_8
        path: data/flowers-102/metaclip_vith14_l2_8-*.parquet
      - split: metaclip_vith14_linf_4
        path: data/flowers-102/metaclip_vith14_linf_4-*.parquet
      - split: metaclip_vith14_linf_8
        path: data/flowers-102/metaclip_vith14_linf_8-*.parquet
      - split: metaclip_vith14_linf_30
        path: data/flowers-102/metaclip_vith14_linf_30-*.parquet
      - split: siglip2_so400m_l1_75
        path: data/flowers-102/siglip2_so400m_l1_75-*.parquet
      - split: siglip2_so400m_l1_300
        path: data/flowers-102/siglip2_so400m_l1_300-*.parquet
      - split: siglip2_so400m_l2_2
        path: data/flowers-102/siglip2_so400m_l2_2-*.parquet
      - split: siglip2_so400m_l2_8
        path: data/flowers-102/siglip2_so400m_l2_8-*.parquet
      - split: siglip2_so400m_linf_4
        path: data/flowers-102/siglip2_so400m_linf_4-*.parquet
      - split: siglip2_so400m_linf_8
        path: data/flowers-102/siglip2_so400m_linf_8-*.parquet
      - split: siglip2_so400m_linf_30
        path: data/flowers-102/siglip2_so400m_linf_30-*.parquet
      - split: siglip2_base_linf_30
        path: data/flowers-102/siglip2_base_linf_30-*.parquet
  - config_name: oxford-iiit-pet
    data_files:
      - split: train
        path: data/oxford-iiit-pet/train-*.parquet
      - split: validation
        path: data/oxford-iiit-pet/validation-*.parquet
      - split: test
        path: data/oxford-iiit-pet/test-*.parquet
      - split: common
        path: data/oxford-iiit-pet/common-*.parquet
      - split: random_linf_30
        path: data/oxford-iiit-pet/random_linf_30-*.parquet
      - split: clip_vitb16_l1_75
        path: data/oxford-iiit-pet/clip_vitb16_l1_75-*.parquet
      - split: clip_vitb16_l1_300
        path: data/oxford-iiit-pet/clip_vitb16_l1_300-*.parquet
      - split: clip_vitb16_l2_2
        path: data/oxford-iiit-pet/clip_vitb16_l2_2-*.parquet
      - split: clip_vitb16_l2_8
        path: data/oxford-iiit-pet/clip_vitb16_l2_8-*.parquet
      - split: clip_vitb16_linf_4
        path: data/oxford-iiit-pet/clip_vitb16_linf_4-*.parquet
      - split: clip_vitb16_linf_8
        path: data/oxford-iiit-pet/clip_vitb16_linf_8-*.parquet
      - split: clip_vitb16_linf_30
        path: data/oxford-iiit-pet/clip_vitb16_linf_30-*.parquet
      - split: clip_vith14_l1_75
        path: data/oxford-iiit-pet/clip_vith14_l1_75-*.parquet
      - split: clip_vith14_l1_300
        path: data/oxford-iiit-pet/clip_vith14_l1_300-*.parquet
      - split: clip_vith14_l2_2
        path: data/oxford-iiit-pet/clip_vith14_l2_2-*.parquet
      - split: clip_vith14_l2_8
        path: data/oxford-iiit-pet/clip_vith14_l2_8-*.parquet
      - split: clip_vith14_linf_4
        path: data/oxford-iiit-pet/clip_vith14_linf_4-*.parquet
      - split: clip_vith14_linf_8
        path: data/oxford-iiit-pet/clip_vith14_linf_8-*.parquet
      - split: clip_vith14_linf_30
        path: data/oxford-iiit-pet/clip_vith14_linf_30-*.parquet
      - split: metaclip_vith14_l1_75
        path: data/oxford-iiit-pet/metaclip_vith14_l1_75-*.parquet
      - split: metaclip_vith14_l1_300
        path: data/oxford-iiit-pet/metaclip_vith14_l1_300-*.parquet
      - split: metaclip_vith14_l2_2
        path: data/oxford-iiit-pet/metaclip_vith14_l2_2-*.parquet
      - split: metaclip_vith14_l2_8
        path: data/oxford-iiit-pet/metaclip_vith14_l2_8-*.parquet
      - split: metaclip_vith14_linf_4
        path: data/oxford-iiit-pet/metaclip_vith14_linf_4-*.parquet
      - split: metaclip_vith14_linf_8
        path: data/oxford-iiit-pet/metaclip_vith14_linf_8-*.parquet
      - split: metaclip_vith14_linf_30
        path: data/oxford-iiit-pet/metaclip_vith14_linf_30-*.parquet
      - split: siglip2_base_linf_30
        path: data/oxford-iiit-pet/siglip2_base_linf_30-*.parquet
  - config_name: stanford_cars
    data_files:
      - split: train
        path: data/stanford_cars/train-*.parquet
      - split: validation
        path: data/stanford_cars/validation-*.parquet
      - split: test
        path: data/stanford_cars/test-*.parquet
      - split: common
        path: data/stanford_cars/common-*.parquet
      - split: random_linf_30
        path: data/stanford_cars/random_linf_30-*.parquet
      - split: clip_vitb16_l1_75
        path: data/stanford_cars/clip_vitb16_l1_75-*.parquet
      - split: clip_vitb16_l1_300
        path: data/stanford_cars/clip_vitb16_l1_300-*.parquet
      - split: clip_vitb16_l2_2
        path: data/stanford_cars/clip_vitb16_l2_2-*.parquet
      - split: clip_vitb16_l2_8
        path: data/stanford_cars/clip_vitb16_l2_8-*.parquet
      - split: clip_vitb16_linf_4
        path: data/stanford_cars/clip_vitb16_linf_4-*.parquet
      - split: clip_vitb16_linf_8
        path: data/stanford_cars/clip_vitb16_linf_8-*.parquet
      - split: clip_vitb16_linf_30
        path: data/stanford_cars/clip_vitb16_linf_30-*.parquet
      - split: clip_vith14_l1_75
        path: data/stanford_cars/clip_vith14_l1_75-*.parquet
      - split: clip_vith14_l1_300
        path: data/stanford_cars/clip_vith14_l1_300-*.parquet
      - split: clip_vith14_l2_2
        path: data/stanford_cars/clip_vith14_l2_2-*.parquet
      - split: clip_vith14_l2_8
        path: data/stanford_cars/clip_vith14_l2_8-*.parquet
      - split: clip_vith14_linf_4
        path: data/stanford_cars/clip_vith14_linf_4-*.parquet
      - split: clip_vith14_linf_8
        path: data/stanford_cars/clip_vith14_linf_8-*.parquet
      - split: clip_vith14_linf_30
        path: data/stanford_cars/clip_vith14_linf_30-*.parquet
      - split: metaclip_vith14_l1_75
        path: data/stanford_cars/metaclip_vith14_l1_75-*.parquet
      - split: metaclip_vith14_l1_300
        path: data/stanford_cars/metaclip_vith14_l1_300-*.parquet
      - split: metaclip_vith14_l2_2
        path: data/stanford_cars/metaclip_vith14_l2_2-*.parquet
      - split: metaclip_vith14_l2_8
        path: data/stanford_cars/metaclip_vith14_l2_8-*.parquet
      - split: metaclip_vith14_linf_4
        path: data/stanford_cars/metaclip_vith14_linf_4-*.parquet
      - split: metaclip_vith14_linf_8
        path: data/stanford_cars/metaclip_vith14_linf_8-*.parquet
      - split: metaclip_vith14_linf_30
        path: data/stanford_cars/metaclip_vith14_linf_30-*.parquet
      - split: siglip2_base_linf_30
        path: data/stanford_cars/siglip2_base_linf_30-*.parquet
  - config_name: uc-merced-land-use-dataset
    data_files:
      - split: train
        path: data/uc-merced-land-use-dataset/train-*.parquet
      - split: validation
        path: data/uc-merced-land-use-dataset/validation-*.parquet
      - split: test
        path: data/uc-merced-land-use-dataset/test-*.parquet
      - split: common
        path: data/uc-merced-land-use-dataset/common-*.parquet
      - split: random_linf_30
        path: data/uc-merced-land-use-dataset/random_linf_30-*.parquet
      - split: clip_vitb16_l1_75
        path: data/uc-merced-land-use-dataset/clip_vitb16_l1_75-*.parquet
      - split: clip_vitb16_l1_300
        path: data/uc-merced-land-use-dataset/clip_vitb16_l1_300-*.parquet
      - split: clip_vitb16_l2_2
        path: data/uc-merced-land-use-dataset/clip_vitb16_l2_2-*.parquet
      - split: clip_vitb16_l2_8
        path: data/uc-merced-land-use-dataset/clip_vitb16_l2_8-*.parquet
      - split: clip_vitb16_linf_4
        path: data/uc-merced-land-use-dataset/clip_vitb16_linf_4-*.parquet
      - split: clip_vitb16_linf_8
        path: data/uc-merced-land-use-dataset/clip_vitb16_linf_8-*.parquet
      - split: clip_vitb16_linf_30
        path: data/uc-merced-land-use-dataset/clip_vitb16_linf_30-*.parquet
      - split: clip_vith14_l1_75
        path: data/uc-merced-land-use-dataset/clip_vith14_l1_75-*.parquet
      - split: clip_vith14_l1_300
        path: data/uc-merced-land-use-dataset/clip_vith14_l1_300-*.parquet
      - split: clip_vith14_l2_2
        path: data/uc-merced-land-use-dataset/clip_vith14_l2_2-*.parquet
      - split: clip_vith14_l2_8
        path: data/uc-merced-land-use-dataset/clip_vith14_l2_8-*.parquet
      - split: clip_vith14_linf_4
        path: data/uc-merced-land-use-dataset/clip_vith14_linf_4-*.parquet
      - split: clip_vith14_linf_8
        path: data/uc-merced-land-use-dataset/clip_vith14_linf_8-*.parquet
      - split: clip_vith14_linf_30
        path: data/uc-merced-land-use-dataset/clip_vith14_linf_30-*.parquet
      - split: metaclip_vith14_l1_75
        path: data/uc-merced-land-use-dataset/metaclip_vith14_l1_75-*.parquet
      - split: metaclip_vith14_l1_300
        path: data/uc-merced-land-use-dataset/metaclip_vith14_l1_300-*.parquet
      - split: metaclip_vith14_l2_2
        path: data/uc-merced-land-use-dataset/metaclip_vith14_l2_2-*.parquet
      - split: metaclip_vith14_l2_8
        path: data/uc-merced-land-use-dataset/metaclip_vith14_l2_8-*.parquet
      - split: metaclip_vith14_linf_4
        path: data/uc-merced-land-use-dataset/metaclip_vith14_linf_4-*.parquet
      - split: metaclip_vith14_linf_8
        path: data/uc-merced-land-use-dataset/metaclip_vith14_linf_8-*.parquet
      - split: metaclip_vith14_linf_30
        path: data/uc-merced-land-use-dataset/metaclip_vith14_linf_30-*.parquet
      - split: siglip2_so400m_l1_75
        path: data/uc-merced-land-use-dataset/siglip2_so400m_l1_75-*.parquet
      - split: siglip2_so400m_l1_300
        path: data/uc-merced-land-use-dataset/siglip2_so400m_l1_300-*.parquet
      - split: siglip2_so400m_l2_2
        path: data/uc-merced-land-use-dataset/siglip2_so400m_l2_2-*.parquet
      - split: siglip2_so400m_l2_8
        path: data/uc-merced-land-use-dataset/siglip2_so400m_l2_8-*.parquet
      - split: siglip2_so400m_linf_4
        path: data/uc-merced-land-use-dataset/siglip2_so400m_linf_4-*.parquet
      - split: siglip2_so400m_linf_8
        path: data/uc-merced-land-use-dataset/siglip2_so400m_linf_8-*.parquet
      - split: siglip2_so400m_linf_30
        path: data/uc-merced-land-use-dataset/siglip2_so400m_linf_30-*.parquet
      - split: siglip2_base_linf_30
        path: data/uc-merced-land-use-dataset/siglip2_base_linf_30-*.parquet

RobustGenBench

A standardized benchmark for robust generalization of image classifiers across common and adversarial ($\ell_1, \ell_2, \ell_\infty$) perturbations.

📄 Paper · 💻 Code · 📜 Citation

RobustGenBench evaluates how well a classifier keeps its accuracy on perturbation types it was not trained against. It provides six downstream classification datasets with fixed splits at 224×224 resolution, together with perturbed copies of every test set:

  • Common corruptions (11 types, severity 3): model-independent, the same images for every model.
  • Random uniform noise ($\ell_\infty \le 30/255$): a control for the adversarial perturbations.
  • Transfer adversarial perturbations (136,180 images): crafted with AutoAttack on 5 open-weight vision-language surrogates, across 7 budgets in $\ell_1$, $\ell_2$, $\ell_\infty$.

Because the adversarial images are pre-computed, any model, including API-only systems such as vision-enabled LLMs, can be evaluated by inference alone, without GPU access. For white-box evaluation, attack your own model on the clean test split.

In total: 5,420 clean test images and 147,020 perturbed test images.

Quick start 

from datasets import load_dataset

# One config per dataset; one split per perturbation setting.
clean = load_dataset("legolasflagstaff/RobustGenBench", "uc-merced-land-use-dataset", split="test")
adv   = load_dataset("legolasflagstaff/RobustGenBench", "uc-merced-land-use-dataset", split="clip_vith14_linf_30")

row = adv[0]
row["image"]                                  # PIL.Image, 224×224 RGB
adv.features["label"].int2str(row["label"])   # class name
row["image_id"]                               # aligns with the clean image of the same id

Evaluate a classifier on every perturbation setting of one dataset:

from datasets import load_dataset, get_dataset_split_names

repo, cfg = "legolasflagstaff/RobustGenBench", "flowers-102"
for split in get_dataset_split_names(repo, cfg):
    if split in ("train", "validation"):
        continue
    ds = load_dataset(repo, cfg, split=split)
    acc = sum(predict(x["image"]) == x["label"] for x in ds) / len(ds)   # your model here
    print(f"{split:24s} {acc:.3f}")

Splits are aligned by image_id: row i of every perturbed split is a perturbed version of row i of test, with the same label.

Datasets

Config Dataset Domain Granularity Classes Train Validation Test
caltech101 Caltech-101 objects coarse 101 5,899 1,042 1,000
fgvc-aircraft-2013b FGVC-Aircraft (2013b) aircraft fine 100 3,334 3,333 1,000
flowers-102 Oxford Flowers-102 flowers fine 102 1,020 1,020 1,000
oxford-iiit-pet Oxford-IIIT Pet pets fine 37 3,128 552 1,000
stanford_cars Stanford Cars cars fine 196 6,922 1,222 1,000
uc-merced-land-use-dataset UC Merced Land Use satellite coarse 21 1,428 252 420

Test sets are standardized to at most 1,000 images by uniform sampling, preserving all classes. All images are resized to 224×224×3 and stored as lossless PNG.

Splits

Each config (dataset) contains the following splits.

Split Content
train, validation Clean images, for fine-tuning.
test Clean test images, the reference for all perturbed splits.
common Each test image with one of 11 common corruptions at severity 3 (column corruption).
random_linf_30 Uniform random noise with $\lVert\delta\rVert_\infty \le 30/255$.
<surrogate>_<norm>_<eps> Transfer adversarial images, e.g. clip_vitb16_linf_30, metaclip_vith14_l1_300.

Common corruptions

Shot noise, impulse noise, defocus blur, motion blur, zoom blur, snow, brightness, contrast, elastic transform, pixelate, JPEG compression, all at severity 3 of the ImageNet-C scale. Test image i receives corruption i mod 11; the assignment is deterministic, so every model is evaluated on identical (image, corruption) pairs.

Adversarial perturbations

Perturbations are crafted with the standard AutoAttack ensemble (APGD-CE, APGD-T, FAB-T, Square) against zero-shot surrogate classifiers. Each surrogate predicts the class whose text embedding ("a photo of a {class}") is closest to the image embedding. AutoAttack only perturbs images the surrogate classifies correctly; other images are copied unchanged (is_modified = false).

Norm Budgets $\epsilon$ Split suffix
$\ell_1$ 75, 300 l1_75, l1_300
$\ell_2$ 2, 8 l2_2, l2_8
$\ell_\infty$ 4/255, 8/255, 30/255 linf_4, linf_8, linf_30

Budgets are expressed on pixel values in $[0, 1]$; the epsilon column stores the exact value (e.g. 0.1176 for 30/255).

Surrogate prefix Model Checkpoint
clip_vitb16 CLIP ViT-B/16 open_clip ViT-B-16 / laion2b_s34b_b88k
clip_vith14 CLIP ViT-H/14 open_clip ViT-H-14 / laion2b_s32b_b79k
metaclip_vith14 MetaCLIP ViT-H/14 open_clip ViT-H-14-quickgelu / metaclip_fullcc
siglip2_so400m SigLIP2 SO400M google/siglip2-so400m-patch14-384
siglip2_base SigLIP2 base google/siglip2-base-patch16-224

Coverage

SigLIP-family surrogates classify the fine-grained datasets poorly, and a surrogate that misclassifies the clean image provides no attack signal. SigLIP2 SO400M is therefore provided on the three coarse-grained datasets only. SigLIP2 base is provided at $\ell_\infty$ 30/255 only; on the fine-grained datasets it leaves most images unmodified (see is_modified).

Split Caltech-101 FGVC-Aircraft (2013b) Oxford Flowers-102 Oxford-IIIT Pet Stanford Cars UC Merced Land Use
clip_vitb16_l1_75 ✓ ✓ ✓ ✓ ✓ ✓
clip_vitb16_l1_300 ✓ ✓ ✓ ✓ ✓ ✓
clip_vitb16_l2_2 ✓ ✓ ✓ ✓ ✓ ✓
clip_vitb16_l2_8 ✓ ✓ ✓ ✓ ✓ ✓
clip_vitb16_linf_4 ✓ ✓ ✓ ✓ ✓ ✓
clip_vitb16_linf_8 ✓ ✓ ✓ ✓ ✓ ✓
clip_vitb16_linf_30 ✓ ✓ ✓ ✓ ✓ ✓
clip_vith14_l1_75 ✓ ✓ ✓ ✓ ✓ ✓
clip_vith14_l1_300 ✓ ✓ ✓ ✓ ✓ ✓
clip_vith14_l2_2 ✓ ✓ ✓ ✓ ✓ ✓
clip_vith14_l2_8 ✓ ✓ ✓ ✓ ✓ ✓
clip_vith14_linf_4 ✓ ✓ ✓ ✓ ✓ ✓
clip_vith14_linf_8 ✓ ✓ ✓ ✓ ✓ ✓
clip_vith14_linf_30 ✓ ✓ ✓ ✓ ✓ ✓
metaclip_vith14_l1_75 ✓ ✓ ✓ ✓ ✓ ✓
metaclip_vith14_l1_300 ✓ ✓ ✓ ✓ ✓ ✓
metaclip_vith14_l2_2 ✓ ✓ ✓ ✓ ✓ ✓
metaclip_vith14_l2_8 ✓ ✓ ✓ ✓ ✓ ✓
metaclip_vith14_linf_4 ✓ ✓ ✓ ✓ ✓ ✓
metaclip_vith14_linf_8 ✓ ✓ ✓ ✓ ✓ ✓
metaclip_vith14_linf_30 ✓ ✓ ✓ ✓ ✓ ✓
siglip2_so400m_l1_75 ✓ – ✓ – – ✓
siglip2_so400m_l1_300 ✓ – ✓ – – ✓
siglip2_so400m_l2_2 ✓ – ✓ – – ✓
siglip2_so400m_l2_8 ✓ – ✓ – – ✓
siglip2_so400m_linf_4 ✓ – ✓ – – ✓
siglip2_so400m_linf_8 ✓ – ✓ – – ✓
siglip2_so400m_linf_30 ✓ – ✓ – – ✓
siglip2_base_linf_30 ✓ ✓ ✓ ✓ ✓ ✓

Data fields

Field Type Description
image image 224×224 RGB PNG.
label class label Ground-truth class (use features["label"].names for names).
image_id int32 Index of the source image within its split; aligns perturbed splits with test.
perturbation string clean, common_corruption_severity3, random_uniform, or transfer_autoattack.
surrogate string Surrogate used to craft the attack (null otherwise).
norm string l1, l2, or linf (null for clean and common).
epsilon float32 Perturbation budget (null for clean and common).
corruption string Common corruption applied (common split only).
is_modified bool Whether the image differs from its clean counterpart.

Evaluation protocol

RobustGenBench reports accuracy per (dataset, perturbation setting), aggregated with:

  • Sum score: sum of accuracies over all (dataset, setting) pairs. Pool-independent; use it to compare new models with published results.
  • Borda score: rank-based consistency across pairs, only meaningful within a fixed pool of models.

The paper uses two sets of settings:

  • Fine-tuned vision models (5 settings): test, common, and white-box AutoAttack on the evaluated model at $\ell_1 = 75$, $\ell_2 = 2$, $\ell_\infty = 4/255$, crafted from the clean test split. Maximum sum score: 30.
  • Black-box models, e.g. vision-enabled LLMs (9 settings): test, common, and the 7 transfer budgets, taking the strongest surrogate per dataset and setting.

Intended use and limitations

Intended use: held-out evaluation of the robustness of image classifiers, fine-tuned or zero-shot, including API-only models. Do not train on the test or perturbed splits.

Limitations

  • Transfer attacks give an upper bound on robust accuracy. They are not a substitute for white-box or adaptive attacks when model access is available.
  • All surrogates are attention-based, so transfer to convolutional targets is weaker.
  • Budgets are not matched across norms; compare norms only at the intensities provided.
  • Common corruptions are evaluated at a single severity.
  • Coverage is limited to six source taxonomies; results do not imply robustness on open-world imagery.

Biases. The source datasets inherit the biases of their collection, for example Western car models (Stanford Cars) and breeds common in the UK (Oxford-IIIT Pet). Some images may incidentally show people or license plates; no additional anonymization was performed.

License

The perturbed images, splits, and metadata produced for RobustGenBench are released under CC BY-NC 4.0. This license does not override the terms of the source datasets: every image is a derivative of an upstream image whose rights remain with its owners. Users must comply with both licenses; where they differ, the more restrictive applies.

Source Homepage Upstream terms
Caltech-101 https://data.caltech.edu/records/mzrjq-6wc02 CC BY 4.0
FGVC-Aircraft (2013b) https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/ non-commercial research use only
Oxford Flowers-102 https://www.robots.ox.ac.uk/~vgg/data/flowers/102/ see upstream terms
Oxford-IIIT Pet https://www.robots.ox.ac.uk/~vgg/data/pets/ CC BY-SA 4.0
Stanford Cars https://ai.stanford.edu/~jkrause/cars/car_dataset.html see upstream terms (research use)
UC Merced Land Use http://weegee.vision.ucmerced.edu/datasets/landuse.html USGS public-domain imagery

Citation 

@inproceedings{heuillet2026robustgenbench,
  title     = {RobustGenBench: A Benchmark for Robust Generalization to Adversarial and Common Perturbations, with Applications to Vision and Vision-Enabled Large Language Models},
  author    = {Heuillet, Maxime and Ngnaw{\'e}, Jonas and Pequignot, Yann and Bhagwatkar, Rishika and Larouche, Alexandre and Gagn{\'e}, Christian and Rish, Irina and Ahmad, Ola and Durand, Audrey},
  booktitle = {TODO},
  year      = {2026}
}

Please also cite the six source datasets.

Versions

  • v2.0 (current): Parquet layout with Dataset Viewer support; restricted to the surrogates reported in the paper.
  • v1.0: original .tar.zst archives, kept for reproducibility under the git tag v1.0-archives.