GenAttack: Practical Black-box Attacks with Gradient-Free Optimization.

This repo has an implemntation for our paper GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

Instructions

Download Inception-v3 model checkpoint

python setup_inception.py

Run 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.

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Maintainer:

• This project is maintained by: Moustafa Alzantot (malzantot)