datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
viyog-adversarial
Viyog — adversarial samples
Precomputed adversarial examples used in Viyog. Attacks: FGSM, BIM, PGD,
APGD-CE (full), plus DeepFool and CW (capped) — crafted against the finetuned
backbones in amanyagami/viyog-weights.
Format — HDF5 (<model>_<attack>.h5): images (uint8, NCHW 224x224) + labels (int32).
root: CIFAR-100 attacks · cifar10/: CIFAR-10 attacks
Load with h5py. Package: pip install viyog · code: https://github.com/amanyagami/viyog
M-Attack-V2-Adversarial-Samples
M-Attack-V2 Adversarial Samples
Adversarial image samples generated by M-Attack-V2, from the paper:
Pushing the Frontier of Black-Box LVLM Attacks via Fine-Grained Detail Targeting
arXiv:2602.17645 | Project Page | Code
Dataset Structure
├── epsilon_8/ # 100 adversarial images (ε = 8/255)
│ ├── 0.png
│ ├── 1.png
│ ├── ...
│ └── metadata.csv
└── epsilon_16/ # 100 adversarial images (ε = 16/255)
├── 0.png
├── 1.png
├── ...
└──… See the full description on the dataset page: https://huggingface.co/datasets/MBZUAI-LLM/M-Attack-V2-Adversarial-Samples.adversarial_pcam
Adversarial PCAM Dataset
This dataset contains adversarial examples generated using various attack techniques on PatchCamelyon (PCAM) images. The adversarial images were crafted to fool the fine-tuned model:lens-ai/clip-vit-base-patch32_pcam_finetuned.
Researchers and engineers can use this dataset to:
Evaluate model robustness against adversarial attacks
Train models with adversarial data for improved resilience
Benchmark new adversarial defense mechanisms
📂… See the full description on the dataset page: https://huggingface.co/datasets/lens-ai/adversarial_pcam.adversarial-mnist
MNIST with Adversarial Examples
This dataset contains MNIST images with both normal and adversarial examples.
The dataset includes:
Original MNIST digit images (28x28 pixels, flattened to 784 features)
Adversarial examples generated from the original images
Labels for digit classification (0-9)
Binary flag indicating whether each sample is adversarial
Features:
label: Digit class (0-9)
pixels 0-783: Flattened 28x28 grayscale pixel values
is_adversarial: Binary flag (0 = normal, 1… See the full description on the dataset page: https://huggingface.co/datasets/wambosec/adversarial-mnist.age-adversarial-attack
Age Adversarial Attack Dataset
Paper: Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation SystemsAuthors: Simiao Ren (Reality Inc. / Duke University)
Overview
This dataset contains 5,809 AI-generated adversarial images derived from a curated set of 329 face images (ages 10–21) drawn from six standard age estimation benchmarks. Each image is a VLM-simulated cosmetic attack designed to make age estimation models misclassify a subject… See the full description on the dataset page: https://huggingface.co/datasets/Scam-AI/age-adversarial-attack.