Add adversarial examples using FGSM

README.md

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+# 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](http://arxiv.org/abs/1312.6199), [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572)
+and [Breaking Convnets](http://karpathy.github.io/2015/03/30/breaking-convnets/).  
+
+## Setup Instructions
+
+1. Clone the repository. 
+```
+git clone https://github.com/divyam3897/adversarial-examples.git
+```
+2. 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](https://www.continuum.io/downloads).
+
+- 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
+```
+
+3. Run the following command to open the jupyter notebook in the browser.
+```
+jupyter notebook
+```
+
+For more resources on Jupyter Notebooks, check out the following:
+* [Installation Documentation](http://jupyter.readthedocs.io/en/latest/install.html)
+* [Trying Jupyter](https://try.jupyter.org/) just in your browser
+* [Jupyter Docs](https://jupyter.readthedocs.io/en/latest/index.html)
+
+## Result
+
+The image below shows 10 samples of 2 on the left, perturbation in the middle and the adversarial examples on the right
+![Adversarial examples](https://github.com/divyam3897/adversarial-examples/blob/master/test.png)