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Testing Robustness Against Unforeseen Adversaries

This repository contains code for evaluating the robustness of image classifiers to a a variety of unforeseen adversaries, as presented in the paper Testing Robustness Against Unforeseen Adversaries. (This repository contains code for the updated version of ImageNet-UA. The code for the original version is at https://github.com/ddkang/advex-uar.)

Samples of the unforeseen attacks

Reproducibility

Comparisons of the Unforeseen Adversarial Accuracy (UAA) of a range of baseline models on ImageNet-UA. See the paper for full results.

Evaluation script

To ensure the reproducibility of our results, we include the eval.sh script, which allows for the recreation of our results from the paper.

bash eval.sh imagenet
bash eval.sh imagenet100
bash eval.sh cifar10

The above evaluates ImageNet, ImageNet100, and CIFAR-10 respectively. Use experiments/eval_imagenet.ipynb, experiments/eval_imagenet100.ipynb, and experiments/eval_cifar10.ipynb to render the above results into bar plots as shown in the appendix.

Additional results:

  • To reproduce the Fourier Analysis results use bash eval.sh fourier_analysis and experiments/fourier_analysis.ipynb to render the plots.
  • Figures of attacked samples in the appendix can be generated using bash eval.sh attacked_samples and experiments/plot_attacked_samples.ipynb.
  • To reproduce the human study results use bash eval.sh humanstudy and experiments/human_study.ipynb.
  • To reproduce the AutoAttack evaluation results use bash experiments/autoattack_dino.sh.

Usage

Command-line interface for model evaluation

The main.py script allows for the evaluation of a model against some specific attack e.g.:

python main.py --log ./log.jsonl --weights weights/imagenet/standard.pt \
		--architecture resnet50 --dataset imagenet \
  		--attack wood --epsilon medium --seed 123  \
    		--attack_config attacks/attack_params_imagenet_default.conf \
      		--step_size=0.005  --num_steps=80 --device cuda

In this case, it has been ran with the following arguments:

  • --attack: String. Specifies the name of the attack against which the model is being evaluated (See the Attack README for a long-form description of all of the attacks).
  • --weights: String. Specifies the path to a ".pt" file which holds a state dictonary of the model weights.
  • --architecture: String. Specifies the name of the architecture used for evaluation.*
  • --dataset: Specifies which dataset is being used for evaluation.
  • --log: Specifies the file to which the experiment results will be appended (see Logging).
  • --epsilon: Specifies the strength of the attack. Can be either a float, or one of "low", "medium" or "high".
  • --attack_config: Specifies the configuration file containing the default hyperparameters for the attacks. We recommend using this in most cases.
  • --step_size: Specifies the size of the steps used in the optimization loop.
  • --num_steps: Specifies the number of steps used in the optimization loop.

for a full list of hyperparameters and their descriptions consult python main.py --help

Running a batch of experiments from a file

To more easily run a batch of experiments, main.py allows a list of experiments (and their hyperparameters) to be given in a jsonlines format.

e.g. given a list of experiments to be ran in a file called "batched_experiments.jsonl":

{ "attack" : "wood", "architecture": "resnet50" , "epsilon": 0.1, ... }
{ "attack" : "snow", "architecture": "resnet50" , "epsilon": 0.3 ... }
...

We sequentially run all experiments in the file:

from main import run_experiment_file
run_experiment_file("./batched_experiments.jsonl")

Logging

When logging we use the jsonlines format, with each experiment appended to the current log location, specified by the ''--log'' command line argument. All experiment hyperparameters (i.e. all command line arguments) are recorded, as well as the robust accuracy, avg. loss, wall-clock experiment time (seconds) and proportion of datapoints for which loss increased after the attack (useful for debugging attacks).

Installation

Install dependencies

git clone https://github.com/centerforaisafety/adversarial-corruptions.git
git submodule update --init --recursive

conda create -n unforeseen_adv python=3.10.9
conda activate unforeseen_adv
pip install -r requirements.txt
pip install git+https://github.com/openai/CLIP.git
pip install git+https://github.com/MadryLab/robustness.git
pip install git+https://github.com/facebookresearch/pytorchvideo.git

Note that MAE models require an older version timm (version 0.3.2), so it needs to be set up separately.

Datasets

Our evaluation covers CIFAR-10, ImageNet, and ImageNet100. The default location for all datasets is in the datasets subdirectory:

$ ls datasets
cifar10  create_imagenet100.sh  imagenet  imagenet100

Once imagenet is in place, imagenet100 can be created by running bash datasets/create_imagenet100.sh.

Model weights

Download model weights from https://drive.google.com/drive/folders/1R4JDUvvWJZ5k3FtxlHFtBq2R-rejOz7b?usp=sharing and put the files in the weights directory (the folder structure needs to be preserved).

Extending the repository

To allow for easy extension of the repository, all imports in main.py are dynamic. This means that the addition of new architectures/models/attacks to the repository is done by adding modules which implement the interfaces expected by main.py. This section details those interfaces.

Adding Attacks

When creating an attack attack_name, which takes the hyperparameters hyperparameter_1, hyperparameter_2 ... we need to:

  1. Create a new python module attacks/attack_name.py.

  2. Ensure that it implements a function called get_attack, taking two arguments:

    • model : nn.Module This is the model for which the attack is being created
    • args: argparse.Namespace This is an object containing the parameters which were passed to main.py (accessed through the named attributes of the object, e.g.

    This should return a attacks.attacks.AttackInstance object which implements the required functionality.

An example of this can be found in attacks/wood.py:

class WoodAttack(attacks.attacks.AttackInstance):

    def __init__(self, model, args):
        super(WoodAttack, self).__init__(model, args)
        self.attack = WoodAdversary(args.epsilon, args.num_steps,
                                    args.step_size, args.wood_noise_resolution,
                                    args.wood_num_rings,args.wood_random_init,normalising_constant=args.wood_normalising_constant)

    def generate_attack(self, batch):
        xs, ys = batch
        return self.attack(self.model, xs, ys)


def get_attack(model, args):
    return WoodAttack(model, args)

After an attack has been added, models can be evaluated against this attack by passing in the --attack attack_name parameter to main.py.

Adding Datasets

Datasets are added by creating new packages in the /models/ directory. To add a dataset called dataset_name.

  1. Create a new python package models/dataset_name/.
  2. Within that package, add a new module models/dataset_name/dataset_name.py.
  3. Within that module, implement a function called get_test_dataset. This takes a single argument args ( a Namespace object containing the parameters passed into main.py) and returns an object satisfying the PyTorch IterableDatasetinterface.

A concrete example can be found in models/imagenet/imagenet.py:

import torchvision
import torchvision.transforms as transforms

import models.imagenet.imagenet_config as imagenet_config

test_transform =  transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
    ])

def get_test_dataset(args):
    test_dataset = torchvision.datasets.ImageNet(imagenet_config.imagenet_location,split="val",transform=test_transform)
    return test_dataset

Note: there is no normalization of the data in the dataset, this is all done in the model.

Adding New Architectures

Model architectures are found in the package of their respective dataset. For example, when adding an architecture called architecture_name which functions on the dataset dataset_name, we should:

  1. Create a new module models/dataset_name/architecture_name.py
  2. Ensure the module implements a function get_model, which takes in a single argument args. This will be an argparse.Namespaceobject containing the parameters passed to the program.

The instance variable args.weights will contain a string denoting the path to a saved ".pt" file, which contains the state dictionary of a saved model. This state dictionary should be loaded into the model, and the relevant torch.nn.Moduleobject should be returned. Care should be taken to load the model into the device specified in config.device.

An example, slightly edited from models/cifar10/resnet50:

def get_model(args):
    model = ResNet(Bottleneck,[3, 4, 6, 3])
    model.load_state_dict(torch.load(weights,map_location=config.device))
    return model

We further note the need to normalize the inputs before loading them into the model, a normalization which is performed in the get_model function within main.py.

Adding new models to the evaluations script

To evaluate a new model, you can make use of the bash eval.sh imagenet or bash eval.sh cifar10 commands. Some modifications are needed for these commands to support the new model:

  • Put the model checkpoint to the appropriate weights subdirectory based on the evaluation dataset.
  • If the model architecture does not already exist, add a new architecture by following adding new architectures. Make sure get_model returns the desired model loaded with the pretrained weights supplied using args.weights. If the architecture already exists, modify get_model to make it support loading the new model.
  • Double check get_test_dataset in models/imagenet/imagenet.py or models/cifar10/cifar10.py to make sure that the correct dataset normalization has been used. Also double check that get_model in main.py has the proper model normalization.
  • Modify models.sh to define a bash variable that contains the checkpoint location of the new model. Append the variable to cifar_models or imagenet_models based on the dataset. Modify get_arch to return the appropriate architecture based on the new model variable.

After these modifications, run bash eval.sh imagenet or bash eval.sh cifar10. This will run the full evaluation on the new model (skipping any other experiments if the corresponding result files exist). The evaluation results will be saved in experiments/results/imagenet or experiments/results/cifar10.

Citation

@article{kaufmann2023testing,
      title={Testing Robustness Against Unforeseen Adversaries}, 
      author={Max Kaufmann and Daniel Kang and Yi Sun and Steven Basart and Xuwang Yin and Mantas Mazeika and Akul Arora and Adam Dziedzic and Franziska Boenisch and Tom Brown and Jacob Steinhardt and Dan Hendrycks},
      year={2023},
      eprint={1908.08016},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}