Official PyTorch implementation of "Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks" (AAAI 2022)

Last update: Dec 27, 2021

Overview

Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks

This is the code for reproducing the results of the paper Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks accepted at AAAI 2022.

Requirements

All Python packages required are listed in requirements.txt. To install these packages, run the following commands.

conda create -n preempt-robust python=3.7
conda activate preempt-robust
pip install -r requirements.txt

Preparing CIFAR-10 data

Download the CIFAR-10 dataset from https://www.cs.toronto.edu/~kriz/cifar.html and place it a directory ./data.

Pretrained models

We provide pre-trained checkpoints for adversarially trained model and preemptively robust model.

adv_l2: ℓ₂ adversarially trained model with early stopping
adv_linf: ℓ_∞ adversarially trained model with early stopping
preempt_robust_l2: ℓ₂ preemptively robust model
preempt_robust_linf: ℓ_∞ preemptively robust model

We also provide a pre-trained checkpoint for a model with randomized smoothing.

gaussian_0.1: model trained with additive Gaussian noises (σ = 0.1)

Shell scripts for downloading these checkpoint are located in ./checkpoints/cifar10/wideresent/[train_type]/download.sh. You can run each script to download a checkpoint named ckpt.pt. To download all the checkpoints, run download_all_ckpts.sh. You can delete all the checkpoints by running delete_all_ckpts.sh.

Preemptively robust training

To train preemptively robust classifiers, run the following commands.

1. ℓ₂ threat model, ε = δ = 0.5

python train.py --config ./configs/cifar10_l2_model.yaml

2. ℓ_∞ threat model, ε = δ = 8/255

python train.py --config ./configs/cifar10_linf_model.yaml

Preemptive robustification and reconstruction algorithms

To generate preepmtive roobust images and their reconstruction, run the following commands. You can specify the classifier used for generating preemptively robust images by changing train_type in each yaml file.

1. ℓ₂ threat model, ε = δ = 0.5

python robustify.py --config ./configs/cifar10_l2.yaml
python reconstruct.py --config ./configs/cifar10_l2.yaml

2. ℓ_∞ threat model, ε = δ = 8/255

python robustify.py --config ./configs/cifar10_linf.yaml
python reconstruct.py --config ./configs/cifar10_linf.yaml

3. ℓ₂ threat model, smoothed network, ε = δ = 0.5

python robustify.py --config ./configs/cifar10_l2_rand.yaml
python reconstruct.py --config ./configs/cifar10_l2_rand.yaml

Grey-box attacks on preemptively robustified images

To conduct grey-box attacks on preemptively robustified images, run the following commands. You can specify attack type by changing attack_type_eval in each yaml file.

1. ℓ₂ threat model, ε = δ = 0.5

python attack_grey_box.py --config ./configs/cifar10_l2.yaml

2. ℓ_∞ threat model, ε = δ = 8/255

python attack_grey_box.py --config ./configs/cifar10_linf.yaml

3. ℓ₂ threat model, smoothed network, ε = δ = 0.5

python attack_grey_box.py --config ./configs/cifar10_l2_rand.yaml

White-box attacks on preemptively robustified images

To conduct white-box attacks on preemptively robustified images, run the following commands. You can specify attack type and its perturbation size by changing attack_type_eval and wbox_epsilon_p in each yaml file.

1. ℓ₂ threat model, ε = δ = 0.5

python attack_white_box.py --config ./configs/cifar10_l2.yaml

2. ℓ_∞ threat model, ε = δ = 8/255

python attack_white_box.py --config ./configs/cifar10_linf.yaml

3. ℓ₂ threat model, smoothed network, ε = δ = 0.5

python attack_white_box.py --config ./configs/cifar10_l2_rand.yaml

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Official PyTorch implementation of "Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks" (AAAI 2022)

Related tags

Overview

Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks

Requirements

Preparing CIFAR-10 data

Pretrained models

Preemptively robust training

1. ℓ2 threat model, ε = δ = 0.5

2. ℓ∞ threat model, ε = δ = 8/255

Preemptive robustification and reconstruction algorithms

1. ℓ2 threat model, ε = δ = 0.5

2. ℓ∞ threat model, ε = δ = 8/255

3. ℓ2 threat model, smoothed network, ε = δ = 0.5

Grey-box attacks on preemptively robustified images

1. ℓ2 threat model, ε = δ = 0.5

2. ℓ∞ threat model, ε = δ = 8/255

3. ℓ2 threat model, smoothed network, ε = δ = 0.5

White-box attacks on preemptively robustified images

1. ℓ2 threat model, ε = δ = 0.5

2. ℓ∞ threat model, ε = δ = 8/255

3. ℓ2 threat model, smoothed network, ε = δ = 0.5

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1. ℓ₂ threat model, ε = δ = 0.5

2. ℓ_∞ threat model, ε = δ = 8/255

1. ℓ₂ threat model, ε = δ = 0.5

2. ℓ_∞ threat model, ε = δ = 8/255

3. ℓ₂ threat model, smoothed network, ε = δ = 0.5

1. ℓ₂ threat model, ε = δ = 0.5

2. ℓ_∞ threat model, ε = δ = 8/255

3. ℓ₂ threat model, smoothed network, ε = δ = 0.5

1. ℓ₂ threat model, ε = δ = 0.5

2. ℓ_∞ threat model, ε = δ = 8/255

3. ℓ₂ threat model, smoothed network, ε = δ = 0.5

This is the Pytorch implementation of Progressive Attentional Manifold Alignment.