Awesome

ReluVal(Usenix Security'18)

ReluVal is a system for formally analyzing self-defined properties on given neural networks by leveraging symbolic interval analysis and iterative refinement.

You can find detailed description of ReluVal in paper Formal Security Analysis of Neural Networks using Symbolic Intervals.

This repository contains the implementation of ReluVal and the evalutions on ACAS Xu described in the paper.

Currently, we have proposed a followup improved system, namely Neurify, proposed in our NIPS 2018 paper Efficient Formal Safety Analysis of Neural Networks. Part of improvements of Neurify have been merged into this repo and successfully improve on average 20 times performance on ACAS Xu dataset compared to ReluVal and achieve on average 5000 times better performance compared to Reluplex. Neurify also supports convolutional models and malware detection models. One can find the implementations of Neurify from Neurify's repo.

Prerequisite

OpenBLAS Installation

OpenBLAS library is used for matrix multiplication speedup. So please make sure you have successfully installed OpenBLAS. You can follow following commands to install openblas or find the quick installation guide at OpenBLAS's Installation Guide.

sudo apt-get install libopenblas-base
wget https://github.com/xianyi/OpenBLAS/archive/v0.3.6.tar.gz
tar -xzf v0.3.6.tar.gz
cd OpenBLAS-0.3.6
make
make PREFIX=/path/to/your/installation install
mv OpenBLAS-0.3.6 OpenBLAS

Downloading

git clone https://github.com/tcwangshiqi-columbia/ReluVal

Compiling:

Please make sure the path of OpenBLAS is the same as the one in MakeFile. Then you can compile ReluVal with following command:

cd ReluVal
make

File Structure

network_test.c: main file to run with
nnet.c: deal with network instance and do symbolic interval analysis
split.c: manage iterative refinement and dynamic thread rebalancing
matrix.c: matrix operations supported by OpenBLAS
nnet/: ACAS Xu models
scripts/: scripts to run the ACAS Xu evaluations reported in paper

Running

The main function is in network_test.c. To run the function, you can call the binary ./network_test. It expects at least three arguments. Here is the argument list:

property: the saftety property want to verify

network: the network want to test with

target: wanted label of the property

need to print = 0: whether need to print the detailed information of each split. 0 is not and 1 is yes. Default value is 0.

test for one run = 0: whether need to estimate the output range without split refinement. 0 is no, 1 is yes. Default value is 0.

check mode = 0: choose the mode of formal anlysis. Normal split and check mode is 0. Check adv mode is 1. Check adv mode will prevent further splits as long as the bisection depth goes upper than 20 so as to locate concrete adversarial examples faster. Default value is 0.

The program will terminate in two ways: (1) a concrete adversarial is found, and (2) the property is verified as safe.

To maximize the performance of ReluVal, I would recommend to quickly run check adv mode first. If there are no adversarial examples located, then run the regular modes.

Example

Here is an example for running ReluVal:

./network_test 5 ./nnet/ACASXU_run2a_1_1_batch_2000.nnet 4

Properties

The ACAS Xu properties reported in the paper are defined in the Appendix A and in the file "properties". One can easily create own properties with following three steps: (1) creating new models in the same style as ones in folder "nnet", (2) adding new bounded input ranges in function load_inputs in file "nnet.c", and (3) adding check function of the property in function check_functions and check_functions1 in file "split.c".

ACAS Xu experiments

The test on ACAS Xu can be easily ran with pre-written scripts in folder "scripts". Here is an example:

./scripts/run_property5.sh

Reporting bugs

Please contact Shiqi Wang (tcwangshiqi@cs.columbia.edu) or submit an issue in https://github.com/tcwangshiqi-columbia/ReluVal/issues.

Citing ReluVal

@inproceedings {shiqi2018reluval,
	author = {Shiqi Wang and Kexin Pei and Justin Whitehouse and Junfeng Yang and Suman Jana},
	title = {Formal Security Analysis of Neural Networks using Symbolic Intervals},
	booktitle = {27th {USENIX} Security Symposium ({USENIX} Security 18)},
	year = {2018},
	address = {Baltimore, MD},
	url = {https://www.usenix.org/conference/usenixsecurity18/presentation/wang-shiqi},
	publisher = {{USENIX} Association},
}

Contributors

License

<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.