This is the official repository for our paper Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness, which is presented at CVPR'21 Workshop on Fair, Data Efficient and Trusted Computer Vision and has received the best paper award.

(Icon made by Eucalyp perfect from www.flaticon.com)

In our recent paper "Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness", we show that numerous fine-tuning methods are vulnerable to adversarial examples based on the pre-trained model. This poses security concerns for indutrial applications that are based on fine-tuning such as Google's Cloud AutoML and Microsoft's Custom Vision.

To combat such attacks, we propose Re-training with noisy feature distillation or Renofeation. Renofeation does not start training from pre-trained weights but rather re-initialize the weights and train with noisy feature distillation. To instantiate noisy feature distillation, we incorporate spatial dropout and stochastic weight averaging with feature distillation to avoid over-fitting to the pre-trained feature without hurting the generalization performance, which in turn improves the robustness.

To this end, we demonstrate empirically that the attack success rate can be reduced from 74.3%, 65.7%, 70.3%, and 50.75% down to 6.9%, 4.4%, 4.9%, and 6.9% for ResNet-18, ResNet-50, ResNet-101, and MobileNetV2, respectively. Moreover, the clean-data performance is comparable to the fine-tuning baseline!

For more details and an ablation study of our proposed method, please check out our paper!

• PyTorch 1.0.0
• TorchVision 0.4.0
• AdverTorch 0.2.0

Layout should be the following for the dataloader to load correctly

CUB_200_2011/
|    README
|    bounding_boxes.txt
|    classes.txt
|    image_class_labels.txt
|    images.txt
|    train_test_split.txt
|--- attributes
|--- images/
|--- parts/
|--- train/
|--- test/

Flower_102/
|    imagelabels.mat
|    setid.mat
|--- jpg/

stanford_dog/
|    file_list.mat
|    test_list.mat
|    train_list.mat
|--- train/
|--- test/
|--- Images/
|--- Annotation/

stanford_40/
|    attributes.txt
|--- ImageSplits/
|--- JPEGImages/
|--- MatlabAnnotations/
|--- XMLAnnotations/

MIT_67/
|    TrainImages.txt
|    TestImages.txt
|--- Annotations/
|--- Images/
|--- test/
|--- train/

Be sure to modify the data path for each datasets for successful runs

The scripts for training and evaluating DELTA, Renofeation, and Re-training are under scripts/.

@InProceedings{Chin_2021_CVPR,
    author    = {Chin, Ting-Wu and Zhang, Cha and Marculescu, Diana},
    title     = {Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2021},
    pages     = {3243-3252}
}