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Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by adding maliciously crafted perturbations to the benign inputs. As a growing number of attacks have been reported to generate adversarial inputs of varying sophistication,…

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Introduction

Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by adding maliciously crafted perturbations to the benign inputs. As a growing number of attacks have been reported to generate adversarial inputs of varying sophistication, the defense-attack arms race has been accelerated.

This project offers the following features to facilitate the development of adversarial attacks and defenses:

  1. It supports ten state-of-the-art attack algorithms with easy-to-use interfaces.
  2. It includes two benchmark datasets (MNIST and CIFAR-10) with their pretrained target models that are widely used in research (7-layer CNN for MNIST and DenseNet40 for CIFAR-10).
  3. It offers a simple interface to generate attack targets systematically.
  4. It keeps track of the progress of generating adversarial examples.

Installation and Dependencies

This project has been developed on a Linux machine with the following configuration:

  • OS: GeForce RTX 2080 SUPER/PCIe/SSE2
  • Processor: Intel® Core™ i7-9700K CPU @ 3.60GHz × 8
  • Memory: 31.2 GiB
  • GPU: GeForce RTX 2080 SUPER/PCIe/SSE2
  • Python Version: 3.6

Ubuntu with GPUs (Recommended)

Create a virtual environment:

python3 -m venv venv

Enter the virtual environment:

source venv/bin/activate

Make sure pip is up-to-date:

python -m pip install --upgrade pip

Install the required libraries:

pip install -r requirements-gpu.txt

Verify your installation by running an attack:

python attack_scripts/FGSM-UA_MNIST_CNN7.py

Ubuntu without GPUs

Create a virtual environment:

python3 -m venv venv

Enter the virtual environment:

source venv/bin/activate

Make sure pip is up-to-date:

python -m pip install --upgrade pip

Install the required libraries:

pip install -r requirements-ubuntu-cpu.txt

Verify your installation by running an attack:

python attack_scripts/FGSM-UA_MNIST_CNN7.py

MacOS without GPUs

It is recommended to use conda for package management in MacOS. The following instructions have been tested on the machines:

  • Macbook Pro (Intel CPU):

    • OS: macOS Big Sur Version 11.2.3
    • Processor: 2.3 GHz Quad-Core Intel Core i7
    • Memory: 32 GiB
  • Macbook Air (Apple M1):

    • OS: macOS Big Sur Version 11.5.2
    • Processor: Apple M1
    • Memory: 8 GiB

Create a virtual environment:

conda create -n gtattackpod python=3.6

Enter the virtual environment:

conda activate gtattackpod

Install the required libraries:

conda install --file requirements-macos-cpu.txt

Verify your installation by running an attack:

python attack_scripts/FGSM-UA_MNIST_CNN7.py

If you encounter an error about an initialized OpenMP, install the following library:

conda install nomkl

While the above steps have been verified on a Macbook Air with an M1 chip, the installation of Tensorflow may cause unexpected errors. We encountered such errors on other MacOS machines. We highly recommend you to run the repository on Ubuntu, ideally with GPU installed.

Supported Attacks

Ten attacks are supported in the current version. The details can be found under the attacks directory. Below shows the list of attack functions you can invoke and their default hyperparameters.

Attack Function Default Parameters
Attack_FastGradientMethod eps=0.3
Attack_BasicIterativeMethod eps=0.3, eps_iter=0.03, nb_iter=40
Attack_ProjectedGradientDescent eps=0.3, eps_iter=0.03, nb_iter=40
Attack_DeepFool overshoot=10.0, max_iter=50
Attack_CarliniL2 confidence=0, max_iterations=10000, learning_rate=1e-2, binary_search_steps=9, initial_const=1e-3, abort_early=True, targeted=True
Attack_CarliniLi confidence=0, max_iterations=1000, learning_rate=5e-3, initial_const=1e-5, largest_const=2e+1, reduce_const=False, decrease_factor=0.9, const_factor=2.0, abort_early=True, targeted=True
Attack_CarliniL0 confidence=.01, max_iterations=1000, learning_rate=1e-2, independent_channels=False, initial_const=1e-3, largest_const=2e6, reduce_const=False, const_factor=2.0, abort_early=True, targeted=True
Attack_JacobianSaliencyMapMethod theta=1.0, gamma=0.1
Attack_EADL1 confidence=0, max_iterations=10000, learning_rate=1e-2, binary_search_steps=9, initial_const=1e-3, abort_early=True, beta=1e-3, targeted=True
Attack_EADEN confidence=0, max_iterations=10000, learning_rate=1e-2, binary_search_steps=9, initial_const=1e-3, abort_early=True, beta=1e-3, targeted=True

Content

This section provides a guideline to use this project. Scripts for generating adversarial examples for MNIST and CIFAR10 can be found in the folders attack_scripts. It can be divided into three parts: (i) selecting test images and generating their attack targets, (ii) generating adversarial examples, and (iii) evaluating the generated adversarial examples. More details can be found in the source code.

Before generating the test set, we load the MNISTDataset dataset that contains the entire training (60,000 images) and test sets (10,000 images). We include a 7-layer CNN model trained to classify the handwritten digits in the MNIST dataset as the victim to be attacked.

dataset = MNISTDataset()
model = MNIST_carlini()

(i) Selecting test images and generating attack targets

Adversarial attacks can be an expensive process and one may only select a subset of images for experimental studies. The helper function get_data_subset_with_systematic_attack_labels selects the first num_examples images from the entire test set that can be correctly classified by the target model. If balanced is set to be True, the same number of images will be selected from each class.

This function also generate attack targets systematically which will be used in targeted attacks. The supported targets are

  1. Most-likely (ML) targets: For each selected test image, we take the class label with the second largest confidence predicted by the target model.
  2. Least-likely (LL) targets: For each selected test image, we take the class label with the lowest confidence predicted by the target model.
X_test, Y_test, Y_test_target_ml, Y_test_target_ll = get_data_subset_with_systematic_attack_labels(
    dataset=dataset, model=model, balanced=True, num_examples=100
)

(ii) Generate adversarial examples

We can easily launch any supported attack by providing the hyperparameters (e.g., the maximum perturbation eps in the FastGradientMethod). Then, the attack(model, X_test, Y_test) function generates adversarial examples. Note that for targeted attacks, Y_test is the attack targets while for untargeted attacks, Y_test is the true class labels.

fgsm = Attack_FastGradientMethod(eps=0.3)
X_test_adv = fgsm.attack(model, X_test, Y_test)

(iii) Evaluate the generated adversarial examples

After generating adversarial examples, we provide a simple interface to generate some statistics to evaluate attacks, which can be done easily with the following function. It provides the misclassification rate, attack success rate, mean confidence on successful adversarial examples, and distortion measured in three different norms.

evaluate_adversarial_examples(X_test=X_test, Y_test=Y_test,
                              X_test_adv=X_test_adv, Y_test_adv_pred=model.predict(X_test_adv),
                              Y_test_target=Y_test, targeted=False)

After running the above, you will see the following:

Loading the dataset...
Evaluating the target model...
Test accuracy on benign examples 99.43%
Mean confidence on ground truth classes 99.39%
Selected 100 examples.
Test accuracy on selected benign examples 100.00%
Mean confidence on ground truth classes, selected 100.00%

---Statistics of FGSM Attack (0.002426 seconds per sample)
Success rate: 46.00%, Misclassification rate: 46.00%, Mean confidence: 94.97%
Li dist: 0.3020, L2 dist: 5.9213, L0 dist: 56.2%

Status

We are continuing the development and there is ongoing work in our lab regarding adversarial attacks and defenses. If you would like to contribute to this project, please contact Ka-Ho Chow.

The code is provided as is, without warranty or support. If you use our code, please cite:

@inproceedings{chow2019denoising,
  title={Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks," IEEE International Conference on Big Data},
  author={Chow, Ka-Ho and Wei, Wenqi and Wu, Yanzhao and Liu, Ling},
  booktitle={Proceedings of the 2019 IEEE International Conference on Big Data},
  year={2019},
  organization={IEEE}
}

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Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by adding maliciously crafted perturbations to the benign inputs. As a growing number of attacks have been reported to generate adversarial inputs of varying sophistication,…

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