Implementation of Wasserstein adversarial attacks.

Last update: Oct 6, 2022

Related tags

Deep Learning fast-wasserstein-adversarial

Overview

Stronger and Faster Wasserstein Adversarial Attacks

Code for Stronger and Faster Wasserstein Adversarial Attacks, appeared in ICML 2020. This repository contains the implementation of our Wasserstein adversarial attacks and pretrained robust models. The implementation of the projection operator and the linear minimization oracle for Wasserstein constraint can be of independent interest.

Instructions for running the code

Dependency: PyTorch 1.5.1 with CUDA 10.2, scipy 1.5.0, and advertorch 0.2.3

Before running the procedure, it is required to install the sparse tensor package:

cd sparse_tensor
python setup.py install

The sparse tensor package includes several functions for initialization of sparse tensors.

Checkout the following bash scripts for different attack methods:

bash bash/test_sinkhorn.sh             # projected Sinkhorn
bash bash/test_projected_gradient.sh   # PGD with dual projection
bash bash/test_frank_wolfe.sh          # Frank-Wolfe with dual LMO

You may want to switch to the option download=True in the Line 111 and 124 in data.py for the first run.

The folder ./checkpoints stores all pretrained models. The names of the checkpoints indicate their training methods. For examples, mnist_vanilla.pth and mnist_adv_training.pth are pretrained models directly taken from Wong et al., 2019. mnist_adv_training_attack-frank_eps-0.3.pth is a model adversarially trained by Frank-Wolfe using epsilon=0.3.

Checkout the following bash script for adversarial training using Frank-Wolfe:

bash bash/train.sh

Checkout the following bash script for model clean accuracy and lp adversarial attacks:

bash bash/test.sh

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Comments

undefined symbol: _ZN3c106detail14torchCheckFailEPKcS2_jS2_

Recently, I have read this paper and code. However, I have a question about them. When I run "python train.py", there is the following bug: ...undefined symbol: ZN3c106detail14torchCheckFailEPKcS2_jS2. Is this because of my CUDA version？ Thank you very much.

opened by Weixin-An 1
Implemented the targeted setting for the Frank-Wolfe attack

Implemented the targeted setting for the Frank-Wolfe attack. To achieve this I transformed the problem from maximizing the loss between the prediction of the model on adversarial sample adv_data and the ground truth label y, to minimizing the loss between the prediction of the model on adversarial sample adv_data and the target class label target.

To change from maximization to minimization problem, I reversed the direction of the gradient by multiplying it with -1.

opened by bezirganyan 0

Implementation of Wasserstein adversarial attacks.

Related tags

Overview

Stronger and Faster Wasserstein Adversarial Attacks

Instructions for running the code

You might also like...

Distributional Sliced-Wasserstein distance code

(NeurIPS 2020) Wasserstein Distances for Stereo Disparity Estimation

Code accompanying the paper "Wasserstein GAN"

Dynamical Wasserstein Barycenters for Time Series Modeling

Causal-Adversarial-Instruments - PyTorch Implementation for Developing Library of Investigating Adversarial Examples on A Causal View by Instruments

Super-Fast-Adversarial-Training - A PyTorch Implementation code for developing super fast adversarial training

The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

Comments

undefined symbol: _ZN3c106detail14torchCheckFailEPKcS2_jS2_

Implemented the targeted setting for the Frank-Wolfe attack

Owner

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