KlugerLab / adversarial-examples

Code to reproduce results in the paper "Defending against Adversarial Images using Basis Functions Transformations"

Code to reproduce results in the paper Defending against Adversarial Images using Basis Functions Transformations

Requirements

• Python 3.4 or higher
• TensorFlow 1.2.1
• Cleverhans 2.0
• sklearn
• matlab.engine

Usage

We use Cleverhans to perform Fast Gradient Attack.

Gray-box attack

Set self.setting = 'graybox' and run:

python run_all.py

You can modify which defense/attack methods to use by changing self.defense_list and self.attack_list in config.py.

Black-box attack

Set self.setting = 'blackbox' and run:

python run_all.py

White-box attack

Set self.setting = 'whitebox'

Backward Pass Differentiable Approximation (BPDA)

python src/run_all_bpda.py

Filtered Gradient Attack

Set self.attack_list = ['FGA'] and run:

python src/run_all_fga.py