Adversarial Attacks on Monocular Pose Estimation

This is the official code for the IROS 2022 paper: Adversarial Attacks on Monocular Pose Estimation by Hemang Chawla, Arnav Varma, Elahe Arani and Bahram Zonooz.

This codebase implements the adversarial attacks on monocular pose estimation using SC-Depth as an example repo.

Setup

Setup the conda environment using:

conda env install --name env_adversarial_attacks_pose --file requirements.yml 

Dataset for adversarial attack

For KITTI Raw dataset, download the dataset using this script http://www.cvlibs.net/download.php?file=raw_data_downloader.zip) provided on the official website.
For KITTI Odometry dataset, download the dataset with color images.

After downloading KITTI dataset, please run:

DATASET=path_to_dataset
OUTPUT=path_to_output
STATIC_FILES=data/static_frames.txt
python data/prepare_test_data.py $DATASET --dataset-format 'kitti_raw' --dump-root $OUTPUT --width 832 --height 256 --num-threads 12 --static-frames $STATIC_FILES --with-depth

Download pose_test_set.tar.xz from drive. Then run:

tar xf pose_test_set.tar.xz

Pretrained Models used for adversarial attacks

We use models from SC-Depth Models under resnet50_pose_256

Attacks

We demonstrate untargeted and targeted attack on the pose estimation. We also measure the impact of cross task attacks. Accordingly, the following files can be used.

Atack	Eval	Filename
Untargeted	Pose	pgd_attack_eval_depth.py
	Depth	pgd_attack_eval_pose.py
Target Pose	Pose	vo_targeted_attack_eval_vo.py
	Depth	vo_targeted_attack_eval_depth.py
Target Depth	Pose	depth_targeted_attack_eval_vo.py
	Depth	depth_targeted_attack_eval_depth.py

Untargeted Attack

Example code to run untargeted attack on SC-Depth ckpt for KITTI Sequence 09 are given hereafter:

Evaluate Pose

python pgd_attack_eval_vo.py --sequence 09 --pretrained-posenet path_to_posenet_ckpt --pretrained-dispnet path_to_dispnet_ckpt --dataset-dir path_to_odom_dataset --output-dir path_to_output_dir --save-imgs

Evaluate Depth

python pgd_attack_eval_depth.py  --sequence 09 --pretrained-posenet path_to_posenet_ckpt --pretrained-dispnet path_to_dispnet_ckpt  --dataset-dir path_to_odom_dataset --gt-dir path_to_depth_gt --output-dir path_to_output_dir --save-imgs

Targeted Attack on Pose

Example code to run targeted attack (move backwards) on SC-Depth ckpt for KITTI Sequence 09 are given hereafter:

Evaluate Pose

python vo_targeted_attack_eval_vo.py --sequence 09 --pretrained-posenet path_to_posenet_ckpt --pretrained-dispnet path_to_dispnet_ckpt --dataset-dir path_to_odom_dataset --output-dir path_to_output_dir --save-imgs --target-mode move_backwards

Evaluate Depth

python vo_targeted_attack_eval_vo.py --sequence 09 --pretrained-posenet path_to_posenet_ckpt --pretrained-dispnet path_to_dispnet_ckpt --dataset-dir path_to_odom_dataset --gt-dir path_to_depth_gt --output-dir path_to_output_dir --save-imgs --target-mode move_backwards

Targeted Attack on Depth

Example code to run targeted attack (flip vertical) on SC-Depth ckpt for KITTI Sequence 09 are given hereafter:

Evaluate Pose

python depth_targeted_attack_eval_vo.py --sequence 09 --pretrained-posenet path_to_posenet_ckpt --pretrained-dispnet path_to_dispnet_ckpt --dataset-dir path_to_odom_dataset --output-dir path_to_output_dir --save-imgs --target-mode v

Evaluate Depth

python depth_targeted_attack_eval_depth.py --sequence 09 --pretrained-posenet path_to_posenet_ckpt --pretrained-dispnet path_to_dispnet_ckpt --dataset-dir path_to_odom_dataset --gt-dir path_to_depth_gt --output-dir path_to_output_dir --save-imgs --target-mode v

Cite Our Work

If you find the code useful in your research, please consider citing our paper:

@inproceedings{chawlavarma2022adversarial,
	author={H. {Chawla} and A. {Varma} and E. {Arani} and B. {Zonooz}},
	booktitle={2022 IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS)},
	title={Adversarial Attacks on Monocular Pose Estimation},
	location={Kyoto, Japan},
	publisher={IEEE (in press)},
	year={2022}
}

License

This project is licensed under the terms of the MIT license.