---
license: mit
task_categories:
- image-classification
- other
tags:
- mnist
- adversarial-examples
- computer-vision
- robustness
size_categories:
- 100K<n<1M
---

# 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

```python
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.