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Multitask Learning Strengthens Adversarial Robustness

@inproceedings{mao2020multitask,
  author    = {Chengzhi Mao and
               Amogh Gupta and
               Vikram Nitin and
               Baishakhi Ray and
               Shuran Song and
               Junfeng Yang and
               Carl Vondrick},
  title     = {Multitask Learning Strengthens Adversarial Robustness},
  booktitle = {Computer Vision - {ECCV} 2020 - 16th European Conference, Glasgow,
               UK, August 23-28, 2020, Proceedings, Part {II}},
  series    = {Lecture Notes in Computer Science},
  volume    = {12347},
  pages     = {158--174},
  publisher = {Springer},
  year      = {2020},
  url       = {https://doi.org/10.1007/978-3-030-58536-5\_10},
  doi       = {10.1007/978-3-030-58536-5\_10},
}

Demo for Robustness under multitask attack

Download Cityscapes dataset from Cityscapes.

Download pretrained DRN-22 model from DRN model zoo.

Modify the path to data and model in demo_mtlrobust.py.

Run demo to see the trend that model overall robustness is increased when the output dimension increased.

To see the gradient norm measurement of robustness, set get_grad=True,

To see the actually robust accuracy for model, set test_acc_output_dim=False

python demo_mtlrobust.py

which explains why segmentation is inherently robust.

CityScape

Data preprocessing

Run python data_resize_cityscape.py to resize to smaller images.

Train Robust model against single task attack

  1. Set up the path to data in config/drn_d_22_cityscape_config.json

  2. Run cityscape_example.sh to train a main task with auxiliary task for robustness.

Taskonomy

Data Preprocessing

You can use our preprocessed data from preprocessed data

Or do from scratch

  1. Download data from official raw data.

  2. Run python data_resize_taskonomy.py to resize to smaller images.

  3. Rename segment_semantic to segmentsemantic.

Train Robust model against single task attack

  1. Set up the path to data in config/resnet18_taskonomy_config.json

  2. Run taskonomy_example.sh to train a main task with auxiliary task for robustness. For different task, we have different different setup, refer to our paper and supplementary for details.

Model evaluation

We offer our pretrained models to download here: Cityscapes segmentation depth and Taskonomy taskonomy segmentation demo

After setting up the path to your downloaded models in test_cityscapes_seg.py and test_taskonomy_seg.py,

Run python test_cityscapes_seg.py and python test_taskonomy_seg.py for evaluating the robustness of multitask models under single task attacks.

Pretrained models for other tasks for Taskonomy can be downloaded [here, comming soon](comming soon)

Acknowledgement

Our code refer the code at: https://github.com/fyu/drn/blob/master/drn.py Taskonomy https://github.com/tstandley/taskgrouping,

We thank the authors for open sourcing their code.

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