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Understanding Adversarial Flow Robustness

PyTorch implementation of Towards Understanding Adversarial Robustness of Optical Flow Networks.

If this work is useful to you, please consider citing our paper:

@inproceedings{schrodi2022towards,
  title={Towards Understanding Adversarial Robustness of Optical Flow Networks},
  author={Schrodi, Simon and Saikia, Tonmoy and Brox, Thomas},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

Prerequisites

To setup your Python environment follow the steps below:

  1. Clone this repository.

  2. cd into this repository.

  3. Create a conda environment:

conda create --name flow_rob --file conda_requirements.txt

or if your system uses GCC-7 as standard compiler, you can just run

conda create --name flow_rob python=3.8

  1. Activate the conda environment conda activate flow_rob.

  2. Run conda develop "${pwd}" (if you are in the repository) or conda develop /path/to/project/understanding_flow_robustness.

  3. Install PyTorch 1.4.0 (Poetry cannot properly deal with that). Be aware that the CUDA version and GCC version need to match; see here for more information.

bash setup/install_torch.sh
  1. Install poetry:
bash setup/install_poetry.sh

or follow the installation instructions here.

  1. Run poetry install.

  2. Install custom cuda layers (requires CUDA runtime!):

bash setup/install_flownet2_deps.sh
  1. Install Spatial correlation sampler:
pip install spatial-correlation-sampler

If you have problems installing it, please refer to here. Note that we need to re-compile this package if run on different GPU_CCs. There might be soon a better solution, see here. In the meantime, you need to run the following command everytime (unless you use the same GPU_CC):

pip uninstall spatial-correlation-sampler -y
cd models/Pytorch-Correlation-extension
rm -rf *_cuda.egg-info build dist __pycache__
python setup.py install
cd ../..
  1. (Optional) Run pre-commit install.

Preparing the data

  1. By default, we assume the following folder structure:
├── datasets
    ├── KITTI
        ├── 2012_prepared
            ├── 2011_09_26_drive_0001_sync_02
            ├── 2011_09_26_drive_0001_sync_03
            ├── ...
            ├── train.txt
            ├── val.txt
        ├── 2012
            ├── test
            ├── training
        ├── 2015
            ├── test
            ├── training
    ├── FlyingChairs_release
        ├── data
    ├── FlyingThings3D
        ├── frames_cleanpass
        ├── frames_finalpass
        ├── optical_flow
    ├── Sintel
        ├── test
        ├── training

  1. Download the following datasets

    1. The raw KITTI dataset with this script from the official website,
    2. KITTI 2012 (stereo flow),
    3. KITTI 2015 (scene 4low),
    4. Flying Chairs,
    5. Flying Things 3D, and
    6. Sintel.

  2. Run

python3 -m setup.prepare_train_data.py /path/to/KITTI/raw --dataset-format 'kitti' --dump-root /path/to/KITTI/2012_prepared --width 1280 --height 384 --num-threads 1 --with-gt
  1. Create symbolic links (if not saved there already):
mkdir datasets
mkdir datasets/KITTI
ln -s /path/to/KITTI/2012_prepared datasets/KITTI/2012_prepared
ln -s /path/to/KITTI/2012/ datasets/KITTI/2012
ln -s /path/to/KITTI/2015/ datasets/KITTI/2015
ln -s /path/to/FlyingChairs_release/ datasets/FlyingChairs_release
ln -s /path/to/FlyingThings3D/ datasets/FlyingThings3D
ln -s /path/to/Sintel/ datasets/Sintel
  1. Run
python3 setup/setup_dataset_kitti.py

Pretrained models

  1. We assume the following folder structure:
├── pretrained_models
    ├── spynet_models
    ├── adv_kitti2012_pwcnet_ifgsm_l2_0.02.pth
    ├── adv_kitti2012_raft_ifgsm_l2_0.02.pth
    ├── adv_kitti2012_robustFlow_ifgsm_l2_0.02.pth
    ├── FlowNet2_checkpoint.pth.tar
    ├── FlowNet2-C_checkpoint.pth.tar
    ├── FlowNet2-S_checkpoint.pth.tar
    ├── larger_field_3x3_x0_l2.pth
    ├── pwc_net_chairs.pth.tar
    ├── raft_flowNetCEnc_noSeparateContext.pth
    ├── raft-things.pth
    ├── RobustFlowNetC.pth
  1. Download the pretrained models for FlowNetC, FlowNetS, FlowNet2, PWC-Net, RAFT, Robust FlowNetC, FlowNetC trained with our training schedule, adv. trained Robust FlowNetC, RAFT with FlowNetC encoder and no separate context encoder, adv. trained PWC-Net, and adv. trained RAFT. Note that you have to transform the RAFT models to be compatible with PyTorch 1.4; see here for more information. For SPyNet copy the weights from here to pretrained_models/spynet_models.

For convenience, you can run bash setup/download_weights to download all model weights.

  1. Create symbolic links (if not saved there already):
mkdir pretrained_models
ln -s /path/to/FlowNet2-C_checkpoint.pth.tar pretrained_models/FlowNet2-C_checkpoint.pth.tar
ln -s /path/to/FlowNet2-S_checkpoint.pth.tar pretrained_models/FlowNet2-S_checkpoint.pth.tar
ln -s /path/to/FlowNet2_checkpoint.pth.tar pretrained_models/FlowNet2_checkpoint.pth.tar
ln -s /path/to/pwc_net_chairs.pth.tar pretrained_models/pwc_net_chairs.pth.tar
ln -s /path/to/raft-things.pth pretrained_models/raft-things.pth

Patch-based Adversarial Attacks on Optical Flow

Generating Adversarial Patches

To generate adversarial patches, run

python3 patch_attacks/main.py \
    --name $path \
    --valset kitti2012 \
    --workers 1 \
    --flownet $flownet \
    --lr 1000 \
    --epochs 40 \
    --patch-size 0.1329 \
    --seed 42

Analysis & Evaluation

To test the patch across various locations and find the worst one (Section 4.1), run

python3 patch_attacks/test_moving_patch.py \
    --name $path \
    --instance $instance \
    --patch_name $name \
    --flownet $flownet \
    --stride 25 \
    --norotate

To reproduce our experiments in Table 2 (requires previous generation of an adversarial patch), run

python3 patch_attacks/test_replace_features.py \
    --name $path \
    --feature_keys corr conv_redir \
    --flownet FlowNetC \
    --instance $instance \
    --patch_name $patch_name

python3 patch_attacks/test_replace_features.py \
    --name $path \
    --feature_keys corr \
    --flownet FlowNetC \
    --instance $instance \
    --patch_name $patch_name

python3 patch_attacks/test_replace_features.py \
    --name $path \
    --feature_keys conv_redir \
    --flownet FlowNetC \
    --instance $instance \
    --patch_name $patch_name

To create the t-SNE embeddings in Figure 3 and 9, run

python3 patch_attacks/test_patch_embeddings.py \
    --name $path \
    --instance $instance \
    --patch_name $patch_name \
    --flownet $flownet

Handcrafted Patch Attacks

To attack without the need of any optimization (Section 5), run

python3 patch_attacks/test_moving_patch.py \
    --name $path \
    --self_correlated_patch vstripes \
    --patch_size $patch_size \
    --flownet $flownet \
    --stride 25 \
    --norotate

You can run the other scripts for analysis and evaluation accordingly.

Global Adversarial Attacks on Optical Flow

Attacking & Evaluation

To reproduce the experiments for untargeted global white-box attacks (Supplemental Section H), run

python3 global_attacks/run_perturb_model.py \
    --output_path $output_path \
    --dataset kitti2015 \
    --n_height 256 \
    --n_width 640 \
    --output_norm $output_norm \
    --learning_rate $lr \
    --n_step $n_step \
    --perturb_method $perturb_method \
    --flow_loss $flow_loss
--flownet $flownet \
    --write_out \

To reproduce the results for the targeted 42 attack (Figure 11), run

python3 global_attacks/run_perturb_model.py \
    --output_path $output_path \
    --dataset kitti2015 \
    --n_height 256 \
    --n_width 640 \
    --output_norm $output_norm \
    --learning_rate $lr \
    --perturb_method $perturb_method \
    --flownet $flownet \
    --write_out \
    --targeted \
    --arbitrary_gt_index fun

or to attack with an arbitrary GT from KITTI 2015 (Supplemental Section I), run

python3 global_attacks/run_perturb_model.py \
    --output_path /misc/lmbraid19/schrodi/stereopagnosia \
    --dataset kitti2015 \
    --n_height 256 \
    --n_width 640 \
    --output_norm $output_norm \
    --learning_rate $lr \
    --n_step $n_step \
    --perturb_method $perturb_method \
    --flownet $flownet \
    --seed 0 \
    --write_out \
    --flow_loss $flow_loss \
    --targeted
--arbitrary_gt_index $arbitrary_index \

where arbitrary_index can be between 0 and 199.

To generate universal adversarial perturbation (Section 7), run

python3 global_attacks/run_perturb_model.py \
    --output_path $output_path \
    --dataset kitti2015 \
    --n_height 256 \
    --n_width 640 \
    --perturb_method $perturb_method \
    --flownet $flownet \
    --perturb_mode same \
    --write_out

and then to attack, run

python3 $WORKDIR/global_attacks/run_perturb_model.py \
    --output_path $output_path \
    --dataset kitti2015 \
    --flownet $flownet \
    --universal_evaluation \
    --output_norm $output_norm \
    --perturb_method $perturb_method \
    --flow_loss $flow_loss \
    --folder_name $folder_name \
    --epoch_number $epoch_number \
    --n_height 256 \
    --n_width 640 \
    --write_out

To apply common image corruptions and test flow nets, run

python3 global_attacks/run_perturb_model.py \
    --output_path $output_path \
    --dataset kitti2015 \
    --n_height 384 \
    --n_width 1280 \
    --perturb_method $perturb_method \
    --flownet $flownet \
    --perturb_mode same \
    --write_out

Adversarial Training

To train adversarially robust flow nets (Supplemental Section K), run:

python training/train.py --name $name \
    --restore_ckpt pretrained_models/raft-things.pth \
    --adv_train \
    --stage kitti2012 \
    --ckpt_dir $ckpt_dir \
    --gpus 0 1 \
    --num_steps $num_steps \
    --batch_size 1 \
    --lr 0.000125 \
    --image_size 256 640 \
    --wdecay 0.0001 \
    --flownet $flownet \
    --perturb_method $perturb_method \
    --output_norm $output_norm \
    --perturb_learning_rate $perturb_learning_rate \
    --perturb_n_step $perturb_n_step \
    --flow_loss $flow_loss

Acknowledgements

We thank several GitHub users for their contributions which are used in this repository: