A Dual Approach to Scalable Verification of Deep Networks

This repository contains a simple implementation of the dual verification formulation descriped in the paper: "A Dual Approach to Scalable Verification of Deep Networks.", https://arxiv.org/abs/1803.06567.

The code analyses a pre-trained TensorFlow classifier network, and attempts to prove that test examples remain correctly classified for all perturbations of the inputs up to some threshold.

Installation

Deep-verify can be installed with the following command:

pip install git+https://github.com/deepmind/deep-verify

Deep-verify will work with both the CPU and GPU version of tensorflow and dm-sonnet, but to allow for that it does not list Tensorflow as a requirement, so it is necessary to ensure that Tensorflow and Sonnet are installed separately.

Usage

This following command pre-trains a non-robust two-layer classifier on MNIST, and verifies it with epsilon set to 0.02:

cd deep-verify/examples
python verify.py --model=tiny --epsilon=0.02

This following commands use interval-bound-propagation to pre-train a small robust conv-net on MNIST with epsilon set to 0.1, and then verifies it with the same epsilon:

cd interval-bound-propagation/examples
python train.py --model=small --epsilon=0.1 \
    --output_dir=/tmp/small_model --num_steps=60001
cd deep-verify/examples
python verify.py --model=small --epsilon=0.1 \
    --pretrained_model_path=/tmp/small_model/model-60000

Giving credit

If you use this code in your work, we ask that you cite this paper:

Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, and Pushmeet Kohli. "A Dual Approach to Scalable Verification of Deep Networks." in UAI, 2018, pp. 550–559.

Acknowledgements

In addition to the people involved in the original publication, we would like to thank Chongli Qin for her contributions.

Disclaimer

This is not an official Google product.