https://github.com/oscarknagg/adversarial

Creating and defending against adversarial examples
https://github.com/oscarknagg/adversarial

Last synced: 12 months ago
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Creating and defending against adversarial examples

• Host: GitHub
• URL: https://github.com/oscarknagg/adversarial
• Owner: oscarknagg
• Created: 2018-12-04T22:59:01.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-01-06T23:35:11.000Z (over 7 years ago)
• Last Synced: 2025-04-07T08:14:37.781Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 84.4 MB
• Stars: 42
• Watchers: 3
• Forks: 17
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# adversarial

This repository contains PyTorch code to create and defend against

adversarial attacks.

See [this Medium article](https://towardsdatascience.com/know-your-enemy-7f7c5038bdf3)

for a discussion on how to use and defend against

the projected gradient attack.

Example adversarial attack created using this repo.

![PGD Attack](https://github.com/oscarknagg/adversarial/blob/master/assets/pgd_attack_imagenet_example.png)

Cool fact - adversarially trained discriminative (_not generative!_)

models can be used to interpolate between classes by creating

large-epsilon adversarial examples against them.

![MNIST Class Interpolation](https://media.giphy.com/media/NlGeQeG4jUViIcZRAD/giphy.gif)

# Contents

- A Jupyter notebook demonstrating how to use and defend against

the projected gradient attack (see `notebooks/`)

- `adversarial.functional` contains functional style implementations of

a view different types of adversarial attacks

    - Fast Gradient Sign Method - white box - batch implementation

    - Projected Gradient Descent - white box - batch implementation

    - Local-search attack - black box, score-based - single image

    - Boundary attack - black box, decision-based - single imagae

# Setup

## Requirements

Listed in `requirements.txt`. Install with

`pip install -r requirements.txt` preferably in a virtualenv.

## Tests (optional)

Run `pytest` in the root directory to run all tests.