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Adversarial-Patch-Training

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

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# Adversarial Training Against Location-Optimized Adversarial Patches

[arXiv](https://arxiv.org/abs/2005.02313) | [Paper](https://link.springer.com/chapter/10.1007/978-3-030-68238-5_32) | [Code](https://github.com/sukrutrao/adversarial-patch-training) | [Video](https://www.youtube.com/watch?v=Qkszh-wYWoY) | [Slides](https://sukrutrao.github.io/project/adversarial-patch-training/AdversarialTrainingAgainstLocationOptimizedAdversarialPatches_Slides.pdf)

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
* tensorboard

With the exception of Python and PyTorch, all requirements can be installed directly using pip:
```bash
$ pip install -r requirements.txt
```

### Setting the paths

In [`common/paths.py`](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 tensorboard logs (if used).

## Data

Data needs to be provided in the HDF5 format. To use a dataset, use the following steps:
* In [`common/paths.py`](common/paths.py), set `BASE_DATA` to the base path where data will be stored.
* For each dataset, create a directory named `<dataset-name>` in `BASE_DATA`
* Place the following files in this directory:
  * `train_images.h5`: Training images
  * `train_labels.h5`: Training labels
  * `test_images.h5`: Test images
  * `test_labels.h5`: Test labels

A script [create_dataset_h5.py](scripts/create_dataset_h5.py) has been provided to convert data in a comma-separated CSV file consisting of full paths to images and their corresponding labels to a HDF5 file. To use this script, first set `BASE_DATA` in [`common/paths.py`](common/paths.py). If the files containing training and test data paths and labels are `train.csv` and `test.csv` respectively, use:
```bash
$ python scripts/create_dataset_h5.py --train_csv /path/to/train.csv --test_csv /path/to/test.csv --dataset dataset_name
```
where `dataset_name` is the name for the dataset.

## Training and evaluating a model
### Training
To train a model, use:
```bash
$ python scripts/train.py [options]
```

A list of available options and their descriptions can be found by using:
```bash
$ python scripts/train.py -h
```
### Evaluation
To evaluate a trained model, use:
```bash
$ python scripts/evaluate.py [options]
```

A list of available options and their descriptions can be found by using:
```bash
$ python scripts/evaluate.py -h
```

## Using models and attacks from the paper

The following provides the arguments to use with the training and evaluation scripts to train the models and run the attacks described in the paper. The commands below assume that the dataset is named `cifar10` and has 10 classes.

### Models
#### Normal
```bash
$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --cuda --mode normal --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip
```

#### Occlusion
```bash
$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --location random --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 1 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip
```

#### AT-Fixed
```bash
$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_pos 3 3 --mask_dims 8 8 --mode adversarial --location fixed --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 25 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip
```

#### AT-Rand
```bash
$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --location random --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 25 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip
```

#### AT-RandLO
```bash
$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --location random --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 25 --optimize_location --opt_type random --stride 2 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip
```

#### AT-FullLO
```bash
$ python scripts/train.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --location random --exclude_box 11 11 10 10 --epsilon 0.1 --signed_grad --max_iterations 25 --optimize_location --opt_type full --stride 2 --log_dir logs --snapshot_frequency 5 --models_dir models --use_tensorboard --use_flip
```

### Attacks

The arguments used here correspond to using 100 iterations and 30 attempts. These can be changed by appropriately setting `--iterations` and `--attempts` respectively.

#### AP-Fixed
```bash
$ python scripts/evaluate.py --cuda --dataset cifar10 --n_classes 10 --mask_pos 3 3 --mask_dims 8 8 --mode adversarial --log_dir logs --models_dir models --saved_model_file model_complete_200 --attempts 30 --location fixed --epsilon 0.05 --iterations 100 --signed_grad --perturbations_file perturbations --use_tensorboard
```

#### AP-Rand
```bash
$ python scripts/evaluate.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --log_dir logs --models_dir models --saved_model_file model_complete_200 --attempts 30 --location random --epsilon 0.05 --iterations 100 --exclude_box 11 11 10 10 --signed_grad --perturbations_file perturbations --use_tensorboard
```

#### AP-RandLO
```bash
$ python scripts/evaluate.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --log_dir logs --models_dir models --saved_model_file model_complete_200 --attempts 30 --location random --epsilon 0.05 --iterations 100 --exclude_box 11 11 10 10 --optimize_location --opt_type random --stride 2 --signed_grad --perturbations_file perturbations --use_tensorboard
```

#### AP-FullLO
```bash
$ python scripts/evaluate.py --cuda --dataset cifar10 --n_classes 10 --mask_dims 8 8 --mode adversarial --log_dir logs --models_dir models --saved_model_file model_complete_200 --attempts 30 --location random --epsilon 0.05 --iterations 100 --exclude_box 11 11 10 10 --optimize_location --opt_type full --stride 2 --signed_grad --perturbations_file perturbations --use_tensorboard
```

## Citation

Please cite the paper as follows:
```
@InProceedings{Rao2020Adversarial,
author = {Sukrut Rao and David Stutz and Bernt Schiele},
title = {Adversarial Training Against Location-Optimized Adversarial Patches},
booktitle = {Computer Vision -- ECCV 2020 Workshops},
year = {2020},
editor = {Adrien Bartoli and Andrea Fusiello},
publisher = {Springer International Publishing},
address = {Cham},
pages = {429--448},
isbn = {978-3-030-68238-5}
}
```

## Acknowledgement

This repository uses code from [davidstutz/confidence-calibrated-adversarial-training](https://github.com/davidstutz/confidence-calibrated-adversarial-training).

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Adversarial-Patch-Training

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