This code repo contains codebase for our proposed TRS ensemble training. We also include other STOA baseline code for fair comparison.
train/Empirical
folder contains corresponding code to construct above robust ensemble models. You can use the command as
python train/Empirical/train_xxx.py **kwargs
**kwargs
refers to the training parameters which is defined in utils/Empirical/arguments.py
eval/Empirical
folder contains:
whitebox/blackbox.py
: Test the whitebox/blackbox attack robustness of the given ensemble model.decison_boundary.py
: Plot the decision boundary figure around the given input instancestrans_matrix.py
: Evaluate the adversarial transferability among base models under various attacks.utils/Empirical
folder contains:
surrogate.py
: Generate blackbox transfer attack instances from the given surrogate ensemble models.