The demo code for the application of the generated patches on a batch from the Imagenet dataset.
Paper: Preprint available at https://arxiv.org/abs/2203.04412
Tutorial: You can find a detailed example of how to use ImageNet-Patch in your model predictions in this script and our tutorial notebook.
You can find the patches saved in assets/patches.gz in the form (patches, targets), where patches and targets are pytorch tensors respectively with shape (10, 3, 224, 224) and (10,). To load them you can simply run the following code snippet and select your favourite one :)
# Load the patches
with gzip.open(os.path.join(os.getcwd(), "assets/patches.gz"), 'rb') as f:
patches, targets = pickle.load(f)
patch = patches[1] # get the patch with id=1
target = targets[1]Once a patch is selected it can be used instantiating the ApplyPatch module in the preprocessing stack, just before the normalizer! To do that you have to specify the patch tensor and the affine transformations ranges (translation_range, rotation_range, scale_range), for which the parameters will be random sampled when an image is taken.
from transforms.apply_patch import ApplyPatch
from torchvision.transforms import Resize, CenterCrop, ToTensor, Normalize, Compose
# Instantiate the ApplyPatch layer, setting patch, target and transforms
apply_patch = ApplyPatch(patch,
translation_range=(.2, .2), # translation fraction wrt image dimensions
rotation_range=45, # maximum absolute value of the rotation in degree
scale_range=(0.5, 1) # scale range wrt image dimensions
)
# Build the preprocessing stack with ApplyPatch before the normalization step
preprocess = Compose([Resize(256), CenterCrop(224), ToTensor(),
apply_patch,
Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
If you use Imagenet-Patch in your work, please cite us using the following BibTeX entry:
@article{Pintor2022ImageNetPatchAD,
title={ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches},
author={Maura Pintor and Daniele Angioni and Angelo Sotgiu and Luca Demetrio and Ambra Demontis and Battista Biggio and Fabio Roli},
journal={ArXiv},
year={2022},
volume={abs/2203.04412}
}