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Robust fine-tuning

"Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"
Francesco Croce, Matthias Hein
ICML 2022
https://arxiv.org/abs/2105.12508

We propose to i) use adversarial training wrt Linf and L1 (alternating the two threat models) to achieve robustness also to L2 and ii) fine-tune models robust in one Lp-norm to get multiple norm robustness or robustness wrt another Lq-norm.

Code

Training code

The file train.py allows to train or fine-tune models. For adversarial training use --attack=apgd, otherwise standard training is performed. The main arguments for adversarial training are (other options in train.py)

  • --l_norms='Linf L1', the list (as string with blank space separated items) of Lp-norms, even just one, to use for training (note that the training cost is the same regardless of the number of threat models used),
  • --l_eps, list of thresholds epsilon for each threat model for training (if not given, the default values are used), sorted as the corresponding norms.
  • --l_iters, list of iterations in adversarial training for each threat model (possibly different), or --at_iter, number of steps for all threat models.

For training new models a PreAct ResNet-18 is used, by default with softplus activation function.

Fine-tuning existing models

To fine-tune a model add the --finetune_model flag, --lr-schedule=piecewise-ft to set the standard learning rate schedule, --model_dir=/path/to/pretrained/models where to download or find the models.

  • We provide here pre-trained ResNet-18 robust wrt Linf, L2 and L1, which can be loaded specifying --model_name=pretr_L*.pth (insert the desired norm).
  • It is also possible to use models from the Model Zoo of RobustBench with --model_name=RB_{} inserting the identifier of the classifier from the Model Zoo (these are automatically downloaded). Note that models trained with extra data should be fine-tuned with the same (currently not supported in the code).

Evaluation code

With --final_eval our standard evaluation (with APGD-CE and APGD-T, for a total of 10 restarts of 100 steps) is run for all threat models at the end of training. Specifying --eval_freq=k a fast evaluation is run on test and training points every k epochs.

To evaluate a trained model one can run eval.py with --model_name as above for the pretrained model or --model_name=/path/to/checkpoint/ for new or fine-tuned classifiers. If the run has the automatically generated name, the corresponding architecture is loaded. More details about the options for evaluation in eval.py.

Credits

Parts of the code in this repo is based on