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Codes for CVPR 2022 Paper: "Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond"

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Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond

Code for CVPR 2022 Paper: "Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond" by Qibing Ren, Qingquan Bao, Runzhong Wang, and Junchi Yan.

News

03/27/2022 - Our code is released.

Requisite

The codes are modified based on ThinkMatch and the basic environment settings also follows it. Here we recommend users to utlize Docker for a quick setup of environments. As for maunal configuration, please refer to ThinkMatch for details.

Docker (RECOMMENDED) from ThinkMatch

  1. We maintain a prebuilt image at dockerhub: runzhongwang/thinkmatch:torch1.6.0-cuda10.1-cudnn7-pyg1.6.3. It can be used by docker or other container runtimes that support docker images e.g. singularity.
  2. We also provide a Dockerfile to build your own image (you may need docker and nvidia-docker installed on your computer).

What is in this repository

  1. train_eval.py is about the robust training pipeline while eval.py is the evaluation codes.
  2. For attack_utils.py, it defines the class AttackGM that implements our locality attack including several attack baselines.
  3. Moreover, src/loss_func.py implements our regularization loss via the parent class GMLoss.
  4. src/utils/config.py defines a global hyper-parameter dictionary cfg, which is referenced everywhere in this project.

Run the Experiment

Run training and evaluation

python train_eval.py --cfg path/to/your/yaml

and replace path/to/your/yaml by path to your configuration file. For example, to reproduce the ASAR-GM (config 1):

python train_eval.py --cfg experiments/config1.yaml

For reproducibility, we release the three configurations of our ASAR-GM in experiments/, namely config1.yaml, config2.yaml, and config3.yaml respectively.

To perform various while-box attacks shown in Paper, run the fllowing script:

python train_eval.py --cfg experiments/eval.yaml

Note that white-box attack evaluation can be automatically performed by setting

EVAL.MODE: all

To customize your attack, please change the value of EVAL.MODE as single.

Additionally, to perform various black-box attacks shown in Paper, run the fllowing script:

python eval.py --cfg experiments/eval_blackbox.yaml --black

Note that you need to specify the model path to the variable PRETRAINED_PATH for model parameters being loaded. Your are welcome to try your own configurations. If you find a better yaml configuration, please let us know by raising an issue or a PR and we will update the benchmark!

Pretrained Models

RobustMatch provides pretrained models of the three configurations of ASAR-GM. The model weights are available via google drive.

To use the pretrained models, firstly download the weight files, then add the following line to your yaml file:

PRETRAINED_PATH: path/to/your/pretrained/weights

Citing this work

@inproceedings{ren2022appearance,
    title={Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond},
    author={Qibing Ren and Qingquan Bao and Runzhong Wang and Junchi Yan},
    booktitle={CVPR},
    year={2022}
}

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Codes for CVPR 2022 Paper: "Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond"

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