libadver

Package for adversarial attack in pytorch

Installation

We developed libadver under Python 3.6 and PyTorch 1.0.0 & 0.4.1. To install libadver, simply run

1.clone repo

git clone https://github.com/selous123/libadver.git

2.install

python setup.py install
&&
pip install .
//pip install -e . ##editable mode

Examples

test PGD Attack

import numpy as np
import torch
pgd_params = {
            'ord': np.inf,
            'y': None,
            'eps': 16.0 / 255,
            'eps_iter': 2.55 / 255,
            'nb_iter': 40,
            'rand_init': True,
            'rand_minmax': 16.0 / 255,
            'clip_min': 0.,
            'clip_max': 1.,
            'sanity_checks': True
        }


import libadver.attack as attack

PGDAttack = attack.ProjectGradientDescent(model = pretrained_clf)

correct = 0
total = 0
for image,label in testloader:
    image, label = image.cuda(), label.cuda()
    ## non targeted
    pgd_params['y'] = label
    pgd_params['clip_min'] = torch.min(image) 
    pgd_params['clip_max'] = torch.max(image)
    
    adv_x = PGDAttack.generate(image, **pgd_params)

    outputs, _, _ = pretrained_clf(adv_x)
    pred_adv = torch.argmax(outputs, dim = 1)
    
    for c in range(3):
        adv_x.data[:,c,:,:] = (adv_x.data[:,c,:,:] * std[c]) + mean[c]
        image.data[:,c,:,:] = (image.data[:,c,:,:] * std[c]) + mean[c]
    torchvision.utils.save_image(adv_x, "adv.jpg", nrow = 4)
    torchvision.utils.save_image(image, "image.jpg", nrow = 4)

    total = total + image.size(0)
    correct = correct + label.eq(pred_adv).sum()
    print("ACC: %.4f (%d, %d)" %(float(correct) / total, correct, total))

For runable examples, see from projected_gradient_descent.py

List of Attack

1.FGSM

2.PGD

3.deepfool

4.universal adversarial

5.generative adversarial

6....

Benchmark for robustness

1.Benchmark_C

The method for creating benchmark_c must be either "gaussian_noise", "shot_noise", "impulse_noise", "defocus_blur", "glass_blur", "motion_blur", "zoom_blur", "snow", "frost", "fog", "brightness", "contrast", "elastic_transform", "pixelate", "jpeg_compression", "speckle_noise", "gaussian_blur", "saturate".

The severity of noise must be either 1,2,3,4,5

examples

import libadver.benchmark as benchmark
import torch
import torchvision.transforms as transforms

dataroot = "/home/lrh/dataset/ISIC_data_2016/robustness"
saveroot = "/home/lrh/dataset/ISIC_data_2016/robustness_c_test/"

params = {
    "method" : "snow",
    "severity" : 2,
}
params["dataroot"] = dataroot
params["saveroot"] = saveroot
print(params)

print("making Benchmark_C.....")
Benchmark_C_dataset = benchmark.Benchmark_C_Generator(transform=transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224)]),**params)
Benchmark_C_dataset_loader = torch.utils.data.DataLoader(Benchmark_C_dataset, batch_size=10, shuffle=False, num_workers=4)

for _ in Benchmark_C_dataset_loader: continue
print("\ndone")

2.Benchmark_P

The method for creating benchmark_p must be either "gaussian_noise", "shot_noise", "motion_blur", "zoom_blur", "snow", "brightness", "rotate","tilt","scale","translate" ,"spatter","speckle_noise", "gaussian_blur", "saturate","shear".

The frameNum for benchmark_p must be a positive integer.

examples

import libadver.benchmark as benchmark
params = {
        "dataroot" : "/home/lrh/dataset/ISIC_data_2016/robustness/",
        "saveroot" : "/home/lrh/dataset/ISIC_data_2016/robustness_p",
        "method" : "shear",
        "frameNum": 5
}


print("making Benchmark_P.....")
benchmark.Benchmark_P_Generator(**params)

print("\ndone")

Comming soon

1. defence

License

This project is licensed under the GNU General Public License v3.0. The terms and conditions can be found in the LICENSE files.

Contribution

Tao Zhang (lrhselous@nuaa.edu.cn)

Mengting Xu