This repo has an implemntation for our paper GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
Install the required libraries:
pip install -r requirements.txt
Download Inception-v3 model checkpoint
python setup_inception.py
You can download test images from ImageNet test set.
To run the attack without dimensionality reduction and adaptive parameter scaling
python main.py --input_dir=./images/ --test_size=1 \
--eps=0.05 --alpha=0.15 --mutation_rate=0.005 \
--max_steps=500000 --output_dir=attack_outputs \
--pop_size=6 --target=704 --adaptive=False
Original class: Squirrl, Adversarial class: Parking Meter, Number of queries=74,171
For more query efficiency
Run attack with dimensionality reduction and adaptive parameter scaling
python main.py --input_dir=./images/ --test_size=1 \
--eps=0.05 --alpha=0.15 --mutation_rate=0.10 \
--max_steps=100000 --output_dir=attack_outputs \
--pop_size=6 --target=704 --adaptive=True --resize_dim=96
Original class: Squirrl, Adversarial class: Parking Meter, Number of queries=11,696
More options:
- If you want to test on a single image, add the FLAG:
--test_example=xx. - To specify a target class, instead of using a random target, add the flag
--target=xx.
First, you need to train the classification models on MNIST and CIFAR-10 datasets.
python train_models.py
python main.py --model=mnist --test_size=1000 --mutation_rate=0.30 --alpha=0.5 --adaptive=False --max_steps=10000 --eps=0.30 --output_dir=mnist_output --pop_size=4 --temp=0.1
python main.py --model=cifar10 --test_size=1000 --mutation_rate=0.05 --alpha=0.25 --adaptive=False --max_steps=10000 --eps=0.05 --output_dir=cifar10_output --pop_size=4 --temp=0.1
- This project is maintained by: Moustafa Alzantot (malzantot)
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