README.md

---
viewer: false
tags:
  - adversarial-robustness
  - image-classification
  - robustness-benchmark
---

# RobustGenBench

A benchmark for evaluating the adversarial robustness of zero-shot image classifiers across six fine-grained / domain-specific datasets and a range of threat models.

A small **stratified sample** (2 images per class) is also available for quick inspection: 👉 https://huggingface.co/datasets/legolasflagstaff/RobustGenBench-sample

## Structure

```
caltech101_processed.tar.zst
fgvc-aircraft-2013b_processed.tar.zst
flowers-102_processed.tar.zst
oxford-iiit-pet_processed.tar.zst
stanford_cars_processed.tar.zst
uc-merced-land-use-dataset_processed.tar.zst

class_names/
  <dataset>.json                 ← integer-label → class-name mappings

adversarial/
  common/common_severity3/<dataset>__common_severity3_processed.tar.zst
  random/linf_eps30_random_uniform/<dataset>__random_linf_eps30_random_uniform_processed.tar.zst
  zeroshot_clip_vitb16_laion2b/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
  zeroshot_clip_vith14_laion2b/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
  zeroshot_metaclip_vith14_fullcc2_5b/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
  zeroshot_siglip2_base_patch16_224/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
  zeroshot_siglip2_so400m_patch14_384/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
  zeroshot_siglip2_so400m_patch16_naflex/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
  zeroshot_siglip2_so400m_patch16_naflex_patchify/<threat_model>/<dataset>__<threat_model>_processed.tar.zst
```

Each archive is a `.tar.zst` containing flat-numbered PNGs and a `labels.csv`.

### Clean archives

```
<dataset>_processed.tar.zst
├── train/
│   ├── 00000.png
│   ├── 00001.png
│   ├── ...
│   └── labels.csv
├── val/
│   ├── 00000.png
│   ├── ...
│   └── labels.csv
├── test/
│   ├── 00000.png
│   ├── ...
│   └── labels.csv
└── metadata.json                ← split counts and number of classes N
```

### Adversarial archives

```
<dataset>__<threat_model>_processed.tar.zst
└── test/
    ├── 00000.png
    ├── ...
    └── labels.csv
```

Filenames are aligned across all archives: `test/00000.png` in every adversarial archive corresponds to the same source image (and same label) used to generate the perturbation. `labels.csv` provides the `filename,label` mapping with integer class indices; resolve to class names via `class_names/<dataset>.json`.

## Datasets

| Dataset | Classes | Test size |
|---|---|---|
| Caltech101 | 101 | 1000 |
| FGVC-Aircraft 2013b | 100 | 1000 |
| Oxford Flowers 102 | 102 | 1000 |
| Oxford-IIIT Pet | 37 | 1000 |
| Stanford Cars | 196 | 1000 |
| UC Merced Land Use | 21 | 420 |

## Threat models

The `adversarial/` tree is organized by **surrogate model used to craft the attack**, then by **threat model**.

**Untargeted attacks (AutoAttack standard, per surrogate):**
- `linf_eps8_autoattack_standard` — L∞, ε = 8/255
- `linf_eps30_autoattack_standard` — L∞, ε = 30/255
- `l2_eps2_autoattack_standard` — L2, ε = 2
- `l2_eps8_autoattack_standard` — L2, ε = 8
- `l1_eps75_autoattack_standard` — L1, ε = 75
- `l1_eps300_autoattack_standard` — L1, ε = 300

**Surrogate-agnostic baselines:**
- `common/common_severity3` — common corruption suite at severity 3
- `random/linf_eps30_random_uniform` — random uniform L∞ noise at ε = 30/255

## Loading

```python
import tarfile, io, csv
import zstandard as zstd
from PIL import Image
from huggingface_hub import hf_hub_download

archive = hf_hub_download(
    repo_id="legolasflagstaff/RobustGenBench",
    repo_type="dataset",
    filename="uc-merced-land-use-dataset_processed.tar.zst",
)

with open(archive, "rb") as f:
    buf = io.BytesIO(zstd.ZstdDecompressor().stream_reader(f).read())
with tarfile.open(fileobj=buf, mode="r:") as tar:
    images = {}
    for m in tar.getmembers():
        if m.name.startswith("test/") and m.name.endswith(".png"):
            images[m.name] = Image.open(io.BytesIO(tar.extractfile(m).read())).convert("RGB")
    labels_f = tar.extractfile(tar.getmember("test/labels.csv"))
    labels = list(csv.DictReader(io.TextIOWrapper(labels_f)))

print(f"Loaded {len(images)} images, {len(labels)} label rows")
```

## Citation

If you use RobustGenBench in your work, please cite:

```bibtex
@inproceedings{robustgenbench2025,
  title  = {RobustGenBench: ...},
  author = {...},
  year   = {2025},
}
```

## License

[Specify license here — e.g. CC-BY-4.0, or per-dataset license inheritance.]