wambosec/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 = adversarial)

This dataset is useful for:

• —Training robust classifiers
• —Studying adversarial examples
• —Evaluating model vulnerability to adversarial attacks
• —Developing adversarial defense mechanisms

Dataset Statistics

• —Training samples: 120,000
• —Normal: 60,000
• —Adversarial: 60,000
• —Test samples: 20,000
• —Normal: 10,000
• —Adversarial: 10,000
• —Features: 784 pixel values + 1 label + 1 adversarial flag
• —Classes: 10 (digits 0-9)

Dataset Structure

The dataset contains the following columns:

• —label: The digit class (0-9)
• —0 to 783: Flattened 28x28 grayscale pixel values (0-255)
• —is_adversarial: Binary flag (0 = normal image, 1 = adversarial image)

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("wambosec/adversarial-mnist")

# Access train and test splits
train_data = dataset['train']
test_data = dataset['test']

# Convert to pandas for easier manipulation
import pandas as pd
train_df = train_data.to_pandas()

# Separate normal and adversarial examples
normal_examples = train_df[train_df['is_adversarial'] == 0]
adversarial_examples = train_df[train_df['is_adversarial'] == 1]

# Get pixel data for visualization
pixels = train_df[[str(i) for i in range(784)]].values
images = pixels.reshape(-1, 28, 28)

Applications

This dataset is useful for:

• —Adversarial robustness research: Training and evaluating robust classifiers
• —Attack detection: Developing methods to detect adversarial examples
• —Defense mechanisms: Testing adversarial defense strategies
• —Benchmarking: Comparing model performance on clean vs adversarial data

Citation

@dataset{ adversarial_mnist, title={MNIST with Adversarial Examples}, author={wambosec}, year={2025}, url={https://huggingface.co/datasets/wambosec/adversarial-mnist} }

Adversarial examples from: https://www.kaggle.com/datasets/sudulakishore/mnist-fgsm Normal MNIST from: https://www.kaggle.com/datasets/hojjatk/mnist-dataset

License

This dataset is released under the mit license.