Reachability Analysis of Deep Neural Networks with Provable Guarantees
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
ExperimentCode wenjie Jun 29, 2018
ExperimentResults wj May 22, 2018
Capture1.PNG w May 22, 2018
Capture2.PNG w May 22, 2018
Capture3.PNG w May 22, 2018
LICENSE Update LICENSE Jun 11, 2018
Long_Version_DeepGo.pdf Wenjie May 8, 2018
README.md Update README.md Oct 5, 2018

README.md

DeepGO: Reachability Analysis of Deep Neural Networks with Provable Guarantees

Reachability Analysis of Deep Neural Networks with Provable Guarantees

Authors: Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska

To be appear in 27th International Joint Conference on Artificial Intelligence (IJCAI'18)

The long version can be found in https://arxiv.org/abs/1805.02242

Email: wenjie.ruan@cs.ox.ac.uk

Abstract

Verifying correctness of deep neural networks (DNNs) is challenging. We study a generic reachability problem for feed-forward DNNs which, for a given set of inputs to the network and a Lipschitz-continuous function over its outputs, computes the lower and upper bound on the function values. Because the network and the function are Lipschitz continuous, all values in the interval between the lower and upper bound are reachable. We show how to obtain the safety verification problem, the output range analysis problem and a robustness measure by instantiating the reachability problem. We present a novel algorithm based on adaptive nested optimisation to solve the reachability problem. The technique has been implemented and evaluated on a range of DNNs, demonstrating its efficiency, scalability and ability to handle a broader class of networks than state-of-the-art verification approaches.

Sample Results

alt text

alt text

alt text

Software

Matlab 2018a

Neural Network Toolbox

Image Processing Toolbox

Parallel Computing Toolbox

Run

Folder "ExperimentCode" contains two sub-folders:

Experiment1_FunctionNN:

Code for the experiment 6.1 in the paper, Lipschitz constant is set before the searching

Experiment2_DNN_MNIST:

Code for the experiment 6.2 in the paper, Lipschitz constant is dynamically estimated during the searching

Folder "ExperimentResults" contains more experimental results

Citation

@article{RHK2018,
	Author = {Wenjie Ruan and Xiaowei Huang and Marta Kwiatkowska},
	Journal = { The 27th International Joint Conference on Artificial Intelligence (IJCAI'18)},
	Title = {Reachability Analysis of Deep Neural Networks with Provable Guarantees},
	Year = {2018}}

or

@article{RHK2018arXiv,
	Author = {Wenjie Ruan and Xiaowei Huang and Marta Kwiatkowska},
	Journal = {arXiv preprint arXiv:1805.02242},
	Title = {Reachability Analysis of Deep Neural Networks with Provable Guarantees},
	Year = {2018}}