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VeriGauge: Unified Toolbox for Representative Robustness Verification Approaches for Deep Neural Networks

Current Maintainer: llylly ([email protected], linyil.com) @ Secure Learning Lab, UIUC

This is a unified toolbox for representative robustness verification approaches for DNNs. The leaderboard for different approaches can be found here: https://github.com/AI-secure/Provable-Training-and-Verification-Approaches-Towards-Robust-Neural-Networks.

Related paper: SoK: Certified Robustness for Deep Neural Networks.

@inproceedings{li2023sok,
    title={SoK: Certified Robustness for Deep Neural Networks},
    author={Linyi Li and Tao Xie and Bo Li},
    booktitle={44th {IEEE} Symposium on Security and Privacy, {SP} 2023, San Francisco, CA, USA, 22-26 May 2023},
    publisher={IEEE},
    year={2023}
}

  • What is robustness verification for DNNs?

    DNNs are vulnerable to adversarial examples. Given a model and an input x0, the robustness verification approaches can certify that there are no adversarial samples around x0 within radius r. The complete verification of DNNs is NP-complete [1,2]. Therefore, current verification approaches usually leverage relaxations, which results in outputting smaller r than the real one.

  • What neural networks are supported?

    Currently, existing approaches mainly support image classification tasks for MNIST, CIFAR-10, and ImageNet, and our toolbox supports all of them though networks for ImageNet are usually too large for standard non-probabilistic verification approaches. Though a significant amount of verification approaches support skip connections, max-pooling layers, etc, typical verification approaches mainly work on feed-forward neural networks with ReLU activations, containing only fully-connected layers and convolutional layers.

Main Features:

  1. A unified lightweight platform for running about 20 verification approaches in a simple PyTorch-based interface.
  2. Easily extensible to your own customized neural networks.
  3. Easily extensible to your own verification approaches.
  4. High-efficiency benefited from the lightweight structure.

[1] Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks. https://arxiv.org/abs/1702.01135.

[2] Towards Fast Computation of Certified Robustness for ReLU Networks. http://proceedings.mlr.press/v80/weng18a.html.

Supported Approach List

Approach Type ClassName Description & Path Comments
Normal Infernace CleanAdaptor The normal inference of the model. Implemented in adaptor.basic_adaptor. Not a verification approach.
Empirical Attack PGDAdaptor Based on Python toolbox cleverhans. Implemented in adaptor.basic_adaptor. Not a verification approach, just provide upper bound of r
Empirical Attack CWAdaptor Based on Python toolbox cleverhans. Implemented in adaptor.basic_adaptor. Not a verification approach, just provide upper bound of r
MILP MILPAdaptor Reimplementation of Tjeng et al's MILP-based verification based on Python's Gurobi API. Adaptor in adaptor.basic_adaptor. Core in basic.fastmilp.MILPVerifier. Complete Verification
SDP PercySDPAdaptor Reimplementation of Raghunathan et al's SDP-based verification based on cvxpy. Adaptor in adaptor.basic_adaptor. Core in basic.percysdp.
SDP FazlybSDPAdaptor Reimplementation of Fazlyb et al's SDP-based verification based on cvxpy. Adaptor in adaptor.basic_adaptor. Core in basic.components.BaselinePointVerifierExt.
Linear-Based FastLinIBPAdaptor Reimplementation of the combination of IBP and FastLin bound ($l$ and $u$ per layer are the maximum or minimum of two bounds respectively). Adaptor in adaptor.basic_adaptor. Core in basic.intervalbound.
Linear-Based FastLinAdaptor Weng et al's linear bound propagation based verification approach. Adaptor in adaptor.recurjac_adaptor. Core in recurjac/.
Linear-Based FastLinSparseAdaptor Weng et al's linear bound propagation based verification approach. This implementation is accelerated by sparse matrix multiplication. Adaptor in adaptor.cnncert_adaptor. Core in cnn_cert/.
Linear-Based CNNCertAdaptor Boopathy et al's linear bound propagation based verification approach. This approach extends FastLin and CROWN to general CNN/Residual/Sigmoid neural networks with high efficiency. Adaptor in adaptor.cnncert_adaptor. Core in cnn_cert/.
Linear-Based LPAllAdaptor LP-full verification approach, which computes $l$ and $u$ layerwise by linear programming. It is mentioned by Boopathy et al, Weng et al, and analyzed by Salman et al. Here, the adaptor is in adaptor.cnncert_adaptor. Core in cnn_cert/ (we use Boopathy et al's implementation).
Linear-Based ZicoDualAdaptor Wong et al's linear dual-based verification approach. Adaptor in adaptor.lpdual_adaptor. Core in convex_adversarial/.
Linear-Based FullCrownAdaptor Zhang et al's linear bound propagation based verification approach. Adaptor in adaptor.crown_adaptor. Core in crown_ibp/.
Linear-Based CrownIBPAdaptor Zhang et al's linear + interval bound propagation based verification approach. Adaptor in adaptor.crown_adaptor. Core in crown_ibp/.
Linear-Based IBPAdaptor Gowal et al's interval propagation based verification approach. Adaptor in adaptor.crown_adaptor. Core in crown_ibp/. The re-implementation of our own is available at adaptor.basic_adaptor.IBPAdaptor, which has similar performance as Zhang et al's and Gowal et al's implementation. For simplicity, by default it uses Zhang et al's implementation.
Lipschitz FastLipAdaptor Weng et al's Lipschitz based verification approach. Adaptor in adaptor.recurjac_adaptor. Core in recurjac/.
Lipschitz RecurJacAdaptor Zhang et al's Lipschitz based verification approach. Adaptor in adaptor.recurjac_adaptor. Core in recurjac/.
Lipschitz SpectralAdaptor Szegedy et al's Lipschitz (spectral bound) based verification approach. Adaptor in adaptor.recurjac_adaptor. Core in recurjac/ (we leverage Zhang et al's implementation).
Branch and Bound AI2Adaptor Gehr et al's branch-and-bound based complete verification approach (concretely, domain of set of polyhedra). Adaptor in adatpro.eran_adaptor. Core in eran/. Complete Verification
Linear-Based DeepPolyAdaptor Singh et al's linear relaxation-based verification approach. Adaptor in adatpro.eran_adaptor. Core in eran/.
Hybrid RefineZonoAdaptor Singh et al's linear relaxation + MILP + IBP hybrid verification approach. Adaptor in adatpro.eran_adaptor. Core in eran/.
Linear-Based KReluAdaptor Singh et al's linear relaxation based verification approach with $l$ and $u$ refinement from multiple neuron's relaxation. Adaptor in adatpro.eran_adaptor. Core in eran/.

Prerequisites

  1. Find a server with GPU support and Linux / MacOS system. (The toolbox has been tested on Linux and MacOS. It should be possible to run on Windows, but we can't guarantee so.)

  2. Prepare necessary datasets:

    1. If you only want to benchmark on MNIST and CIFAR-10, don't need to do anything, since PyTorch will automatically download them later.

    2. If you want to benchmark on ImageNet, in datasets.py, please set the Line 12 to the path of ImageNet dataset on your local environment. The dataset should be organized according to the instruction above Line 12.

  3. Install necessary packages according to requirements.txt.

  4. Download model weights at: https://drive.google.com/drive/folders/1vh7dwvn1P544r5rzOfJFPzShV4-UsTAv?usp=sharing, then store the whole folder as models_weights/exp_models/. Or if you like, you can also train and load your own models.

  5. Set your global Keras settings in ~/.keras/keras.json as below.

{
    "floatx": "float32",
    "epsilon": 1e-07,
    "backend": "tensorflow",
    "image_data_format": "channels_first"
}

  1. Install ELINA, and DeepG according to the instructions in eran/install.sh. Then specify their path in constants.py (Line 5 and Line 6).

  2. Create an empty folder named tmp/ under the tool root path.

Then you are all set!

Main Usage

Replicate Our Results in The Paper

The experiments/run.sh and experiments/run_cifar.sh contain concrete commands for the users to replicate our results.

In the arguments of these commands, --method specifies the verification approaches to run, --dataset specifies the dataset to run, --model specifies the model to run, --weight specifies the model weight to run, and --mode specifies the type of evaluation to run. All of them supports multiple arguments, and multiple settings will be ran sequentially.

  • --method: From experiments/data_analyzer, the dictionary approach_mapper defines the name mapping between paper's verification approach names to argument names.
  • --dataset: It should be names in contants.py's DATASETS list.
  • --model: ranging from A to G, where A = FCNNa, B = FCNNb, C = CNNa, D = CNNb, E = CNNc, F = CNNd, and G = FCNNc.
  • --weight: same names as trained weight settings in the paper.
  • --mode: should be either "verify" - compute the robust accuracy, or "radius" - compute the average certified robustness radius.

Verify Your Own Models

The main script main.py shows how to run the toolkit on your customized tasks.

To enable the support for new verification approaches, you could copy the implementation folder to the project root folder, then write your own adaptor following the adaptor.adaptor.Adaptor template, and strore it in adaptor/ folder.

To enable the support for your own model, you could write your model loading function in models/ (the function should load both structure and weights, and should be PyTorch model), and register the new model in model.py.

  • If the model works on normalized input, you should treat the input normalization as the additional first layer of your model, and this first layer could be implemented by datasets.NormalizeLayer.
  • If the model contains flatten layer, it should be replaced by our flatten layer implementation in models.zoo.Flatten.

To enable the support for new datasets, you could extend datasets.py, and register the dataset name in constants.py.

Base Repos and Tool Structure Overview

Our toolbox is based on following tools, which are stored in respective folders:

Other Folders

To provide a uniform interface for them, we utilize the "adaptor design pattern", where we write a class to provide uniform methods for each approach in these tools. All classes are in folder adaptor/, and inherited from adaptor.adaptor.Adaptor class.

The adaptor.adaptor.Adaptor class should be initialized by dataset and model, and provides two main methods: verify(self, input, label, norm_type, radius) and calc_radius(self, input, label, norm_type, upper=0.5, eps=1e-2).

The verify() method receives the input, true label, Lp norm type, and radius. It returns true or false.

The calc_radius() method receives the input, true label, Lp norm type, the maximum possible radius, and the precision. It by default implements a binary-search based procedure which calls verify() multiples times to compute robustness radius.

The basic/ folder includes our own reimplementation of a few verification approaches.

The experiments/ folder contains our raw experiment data.

The models/ folder contains the full definitions of the model structure used in the experiments. You can extend it to more models.

Other Files

constants.py defines important global constants.

  • If you want to improve the toolbox for more norm types, datasets, or verification approaches, remember to update them here.

datasets.py contains the dataset preparation scripts.

model.py indexes the models defined in models/, it contains a large dictionary, which maps the string indexes to concrete methods in models/ which loads the models.

main.py: the toggle entrance for runing all the verification approaches.

Copyrights

A significant amount of code in this project is embeded and adapted from existing open-sourced repositories. For those code, we keep all the source labels and author tags without modifications. We tried to list all the sources thoroughly above, but may still miss some. If you feel this tool violates your copyright, we apologize in advance and please contact us immediately.

For all other original parts, we allow free distribution of the code under the MIT license.

Future Plans

We plan to provide an uniform interface for C++-based verification approaches, including Reluplex, Neurify, ReluVal, etc.

For recently popular randomized smoothing series approaches, we may provide a separate tool in the future.