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TRS ensemble training code repo

This code repo contains codebase for our proposed TRS ensemble training. We also include other STOA baseline code for fair comparison.

Empirical ensemble robustness

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 instances
  • trans_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.

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