Skip to content
Adversarial examples for MNIST
Jupyter Notebook
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.

Generative Adverserial Networks for MNIST

This is an example of generating adversarial examples to exploit the deep MNIST Convolution network. It is inspired from Intriguing Properties of Neural Networks, Explaining and Harnessing Adversarial Examples and Breaking Convnets.

Setup Instructions

  1. Clone the repository.
git clone
  1. Make sure tha you have Jupyter Notebook installed. You can install Anaconda (which installs Python, Jupyter Notebook, and a bunch of other useful computing libraries) or use pip.
  • To install Anaconda.

  • If you want to install using pip, update pip with the following code (Replace pip with pip3 if using Python 3).

On Linux/Mac OS:

pip install -r requirements.txt

You should be able to run the following.

pip install jupyter
  1. Run the following command to open the jupyter notebook in the browser.
jupyter notebook

For more resources on Jupyter Notebooks, check out the following:


The image below shows 10 samples of 2 on the left, perturbation in the middle and the adversarial examples on the right Adversarial examples

You can’t perform that action at this time.