Verification of Neural Networks: Specifying Global Robustness using Generative Models

Project | arXiv

We present experiments exploring the notions of global correctness and global robustness defined in our research paper:

Nathanaël Fijalkow and Mohit Kumar Gupta

The experiments are in Jupter notebook format:

• Random walk
• Analysis of an image classifier using a generative model
• Evaluating the global correctness
• Searching for Realistic Adversarial Examples: black-box approach
• Searching for Realistic Adversarial Examples: white-box approach
• Dependence on the generative model: disjoint training sets

All experiments use Tensorflow, and pre-trained models can be used (see /Models).

Random Walk results

• Images generated in a random walk:

• Confidence score for images generated in a random walk:

Analysis of an image classifier using a generative model

• Classifier confidence score with images:

• Prediction accuracy (of the image classifer) across the generated images:

Percentage of images with > 90% prediction: 86.84 %

Percentage of images with > 97% prediction: 83.95 %

Percentage of images with > 99% prediction: 81.49 %

Evaluating the Global Correctness

• Outliers (generated during evaluation):

Searching for realistic adversarial examples: White box approach

Searching for realistic adversarial examples: Black box approach

Prerequisites

• Linux or macOS
• Python 3/2
• Tensorflow

Getting Started

Installation

• Install python libraries tensorflow and numpy.

pip3 install tensorflow numpy

• Install Jupyter Notebook

pip3 install jupyter

• Clone this repo:

git clone https://github.com/mohitiitb/NeuralNetworkVerification_GlobalRobustness.git
cd NeuralNetworkVerification_GlobalRobustness

Testing

• All experiments use pre-trained models (see /Models).

• To generate/train a classifier (optional)

python3 train_classifer.py

• To generate/train a GAN (optional)

python3 train_gan.py

• Go through the jupyter notebooks they are self explanatory and easy to run.

Datasets

• MNIST
• Extended MNIST
• Fashion MNIST