A PyTorch implementation of universal adversarial perturbation which is more easy to understand and implement.
Refer to the original tensorflow code.
training dataset(Choose 10 imgs for each category)
Validation dataset
All you need to do is unzip the downloaded file, and the results are as follows:
To get started, you should first Change the training data file path (in search.py) to yours. For example
X = ImagetNet('../data/ILSVRC2012_train/pick_image/', 1000, 10, transforms = transform)If your testing data path (in search.py) is testing_data_path, then you should write
val_loader = torch.utils.data.DataLoader(ImageFolder(testing_data_path, transforms = transform)It should be noted that I use two different ways to input our data (i.e., X and val_loader), the main reason I didn't make it consistent is because I wanted to show you a variety of ways to write it. Hope it doesn't bother you.
After you modify all the path, then you can run the following demo code
python search.py
I tested my code for googlenet and resnet-152 with \epsilon = 10 (L_inf norm), and after 5 epoch, the result accuracy on validation set was ~78% and ~84% respectively. It is similar with the result report in paper.
[1] S. Moosavi-Dezfooli*, A. Fawzi*, O. Fawzi, P. Frossard: Universal adversarial perturbations, CVPR 2017