Adversarial Training Against Location-Optimized Adversarial Patches
Adversarial-Patch-Training
Code for the paper:
Sukrut Rao, David Stutz, Bernt Schiele. (2020) Adversarial Training Against Location-Optimized Adversarial Patches. In: Bartoli A., Fusiello A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science, vol 12539. Springer, Cham. https://doi.org/10.1007/978-3-030-68238-5_32
Setup
Requirements
- Python 3.7 or above
- PyTorch
- scipy
- h5py
- scikit-image
- scikit-learn
Optional requirements
To use script to convert data to HDF5 format
- torchvision
- Pillow
- pandas
To use Tensorboard logging
With the exception of Python and PyTorch, all requirements can be installed directly using pip:
$ pip install -r requirements.txt
Setting the paths
In common/paths.py, set the following variables:
BASE_DATA: base path for datasets.BASE_EXPERIMENTS: base path for trained models and perturbations after attacks.BASE_LOGS: base path for