Sizhe-Chen/AAA

official repository for the NeurIPS 2022 paper "Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks"

Decription

• The code is the official implementation of NeurIPS paper Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
• This repository supports data protection on CIFAR-10 and ImageNet
• The experiments are run in an NVIDIA A100 GPU, but could modify the batch size to run on small GPUs
• Install dependencies

conda env create -f pt.yaml

• Prepare ImageNet validation set (2012), place in folder 'data/ILSVRC2012_img_val'

Numerical results of AAA (Table 2)

• CIFAR-10 (WideResNet28)

python square.py

python square.py --model=Dai2021Parameterizing

python square.py --defense=inRND

python square.py --defense=AAALinear

• ImageNet (WideResNet50)

python square.py --dataset=imagenet --model=wide_resnet50_2 --eps=4

python square.py --dataset=imagenet --model=Salman2020Do_50_2 --eps=4

python square.py --dataset=imagenet --model=wide_resnet50_2 --defense=inRND --eps=4

python square.py --dataset=imagenet --model=wide_resnet50_2 --defense=AAALinear --eps=4

• ImageNet (ResNeXt101)

python square.py --dataset=imagenet --model=resnext101_32x8d --eps=4

python square.py --dataset=imagenet --model=ResNeXt101_DenoiseAll --eps=4

python square.py --dataset=imagenet --model=resnext101_32x8d --defense=inRND --eps=4

python square.py --dataset=imagenet --model=resnext101_32x8d --defense=AAALinear --eps=4

Generalization of AAA (Table 4)

• vanilla training

python square.py --targeted --defense=AAALinear

python square.py --l2 --eps=0.5

• adversarial training

python square.py --targeted --defense=AAALinear --model=Dai2021Parameterizing

python square.py --l2 --eps=0.5 --model=Dai2021Parameterizing

Adaptive attacks of AAA (Table 6)

• bi-Square

python square.py --loss=bi --defense=AAALinear

python square.py --loss=bi --defense=AAASine

• up-Square

python square.py --loss=up --defense=AAALinear

python square.py --loss=up --defense=AAASine

Others

• QueryNet attack (Table 3)

python square.py --num_s=3 --gpu=0,1,2,3 --defense=AAALinear

• Attack by CE-loss (Table 8)

python square.py --loss=ce --defense=AAALinear

Files

├── attacker.py
├── data
│   └── val.txt # for ImageNet attacks
├── dent.py
├── dfdmodels # for ResNeXt101_DenoiseAll
│   ├── adv_model.py
│   ├── inception_resnet_v2.py
│   ├── __init__.py
│   ├── nets.py
│   ├── resnet_model.py
│   └── third_party
│       ├── imagenet_utils.py
│       ├── __init__.py
│       ├── __pycache__
│       │   ├── imagenet_utils.cpython-36.pyc
│       │   ├── imagenet_utils.cpython-37.pyc
│       │   ├── __init__.cpython-36.pyc
│       │   └── __init__.cpython-37.pyc
│       ├── README.md
│       ├── serve-data.py
│       └── utils.py
├── PCDARTS # for querynet attack
│   ├── architect.py
│   ├── genotypes.py
│   ├── __init__.py
│   ├── model.py
│   ├── model_search_imagenet.py
│   ├── model_search.py
│   ├── model_search_random.py
│   ├── operations.py
│   ├── README.md
│   ├── test.py
│   ├── train_imagenet.py
│   ├── train.py
│   ├── train_search_imagenet.py
│   ├── train_search.py
│   ├── utils.py
│   ├── V100_python1.0
│   │   ├── train.py
│   │   └── train_search.py
│   └── visualize.py
├── pt.yaml
├── square.py
├── utils.py
└── victim.py