https://github.com/nesl/adversarial_genattack
https://github.com/nesl/adversarial_genattack
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/nesl/adversarial_genattack
- Owner: nesl
- License: mit
- Created: 2018-05-28T05:02:18.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T22:00:50.000Z (about 3 years ago)
- Last Synced: 2023-10-20T22:01:19.508Z (over 2 years ago)
- Language: Python
- Size: 963 KB
- Stars: 51
- Watchers: 19
- Forks: 14
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://arxiv.org/abs/1805.11090)
## Instructions
### Setup
Install the required libraries:
```
pip install -r requirements.txt
```
### ImageNet Experiment
Download `Inception-v3` model checkpoint
```
python setup_inception.py
```
You can download test images from [ImageNet test set](http://jaina.cs.ucdavis.edu/datasets/adv/imagenet/img.tar.gz).
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`.
---
### MNIST and CIFAR-10 Experiments
First, you need to train the classification models on MNIST and CIFAR-10 datasets.
```
python train_models.py
```
### Attacking MNIST Model
```
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
```
### Attacking CIFAR-10 Model:
```
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
```
## Maintainer:
* This project is maintained by: Moustafa Alzantot [(malzantot)](https://github.com/malzantot)