Testing	Coverage	Documentation

NeuralVerification.jl

This library contains implementations of various methods to soundly verify deep neural networks. In general, we verify whether a neural network satisfies certain input-output constraints. The verification methods are divided into five categories:

• Reachability methods: ExactReach, MaxSens, Ai2,

• Primal optimization methods: NSVerify, MIPVerify, ILP

• Dual optimization methods: Duality, ConvDual, Certify

• Search and reachability methods: ReluVal, DLV, FastLin, FastLip

• Search and optimization methods: Sherlock, BaB, Planet, Reluplex

Reference: C. Liu, T. Arnon, C. Lazarus, C. Barrett, and M. Kochenderfer, "Algorithms for Verifying Neural Networks," arXiv:1903.06758.

Installation

To download this library, clone it from the julia package manager like so:

(v1.0) pkg> add https://github.com/sisl/NeuralVerification.jl

Please note that the implementations of the algorithms are pedagogical in nature, and so may not perform optimally. Derivation and discussion of these algorithms is presented in the survey paper linked above.

Note: At present, Ai2, ExactReach, and Duality do not work in higher dimensions (e.g. image classification). This is being addressed in #9

The implementations run in Julia 1.0.

Example Usage

Choose a solver

using NeuralVerification

solver = BaB()

Set up the problem

nnet = read_nnet("examples/networks/small_nnet.nnet")
input_set  = Hyperrectangle(low = [-1.0], high = [1.0])
output_set = Hyperrectangle(low = [-1.0], high = [70.0])
problem = Problem(nnet, input_set, output_set)

Solve

julia> result = solve(solver, problem)
CounterExampleResult(:violated, [1.0])

julia> result.status
:violated

For a full list of Solvers and their properties, requirements, and Result types, please refer to the documentation.