VizSec17: Web-based visualization tool for adversarial machine learning / LiveDemo
@inproceedings{norton2017adversarial,title={Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning},author={Norton, Andrew P and Qi, Yanjun},booktitle={Visualization for Cyber Security (VizSec), 2017 IEEE Symposium on},pages={1--4},year={2017},organization={IEEE}}
[1] “Adversarial-Playground: A Visualization Suite Showing How Adversarial
Examples Fool Deep Learning”, Andrew P. Norton, Yanjun Qi, arxiv (this is a revised version of [1])
Published in: 2017 IEEE Symposium on Visualization for Cyber Security (VizSec), URL
[2] “Adversarial Playground: A Visualization Suite for Adversarial Sample Generation”, Norton, Andrew and Qi, Yanjun, url
This is Andrew Norton’s capstone research work. The goal is to perform a similar function to Google’s TensorFlow Playground, but for evasion attacks in adversiaral machine learning. It is a web service that enables the user to visualize the creation of adversarial samples to neural networks.
Information regarding the various settings for each attack model may be found the project slide set (see especially the System Demonstration section).
There are git submodules in this repository; to clone all the needed files, please use:
git clone --recursive https://github.com/QData/AdversarialDNN-Playground.git
The primary requirements for this package are Python 3 with Tensorflow version 1.0.1 or greater. The requirements.txt file contains a listing of the required Python packages; to install all requirements, run the following:
pip3 -r install requirements.txt
If the above command does not work, use the following:
pip3 install -r requirements.txt
Or use the following instead if need to sudo:
sudo -H pip install -r requirements.txt
Once you’ve downloaded the repo, run python3 run.py :
$ cd AdversarialDNN-Playground-localDir$ python3 run.py &
Or run the following command to run the webapp in the background even when logged out from the server:
$ cd AdversarialDNN-Playground-localDir$ nohup python3 run.py & # run in background even when logged out
Now use your favorite explorer to navigate to localhost:9000 or ‘your_server_url:9000’
By default, we give the user the option of 11 seed images (one from each class 0 through 9, and one misclassified instance from the “9” class). However, you may desire to select different images for your own instance of this tool. It is quite easy to do so via the json_gen.py script in the utils directory. Edit the images_to_generate.csv file to specify the indices into the MNIST dataset which interest you in the first column, and provide a human readable description in the second column.
After editing the images_to_generate.csv file, run:
$ python images_to_generate.csv
This will take a short amount of time, as it processes and classifies (using the pre-trained model) each seed image, and saves a png file of the image. There are two items created as output, and they must be moved into proper locations in the webapp directories:
seeds.json : This contains each image and the classifier output as a JSON object; it goes in /webapp/models.imgs/ : This directory should be copied into the webapp/static/ directory.Run (or restart) the webserver, and the new options should be visible to the user.
Flask-based server