CMAT

This repository is the official implementation of CMAT:A Cross-Model Adversarial Texture for Scanned Document Privacy Protection.

Install

• Step 1. please follow the installation instructions of T-SEA to create a conda environment

  conda create -n text-attack python=3.7
  conda activate text-attack
  pip install -r requirements.txt

• Step 2. please follow the build-from-source instructions to install mmocr

  pip install -U openmim
  mim install mmcv-full
  pip install mmdet
  cd detlib/mmocr
  pip install -r requirements.txt
  pip install -v -e .

  and then go back to the root of the project，replace lib/python3.7/site-packages/mmdet/models/detectors/base.pywith our ./base.py

• Step 3. please install the following packets

   pip install Image
   pip install jupyter
  
  #  if error about PIL exist, please uninstall pillow and re-install it with lower version
  pip uninstall pillow
  pip install "pillow<7"

Attack

First of all, modify the line 15 of configs/parallel.yaml，add the DETECTOR which you need：

DETECTOR: #add the DETECTOR which you need
 NAME: ["PS_IC15"] #,"PS_CTW","PANET_IC15","PANET_CTW"]
 WEIGHT: [1.0, 1.0, 1.0] #Model loss Weight

Single Model / Multy Model (However, no model loss weighting is performed)：

Please,set all weight to 1.0

and then run train.sh，you can check the results in tensorboard(or results/crop.log), also you will find the perturbation checkpoint in /results/crop.

CUDA_VISIBLE_DEVICES=0 nohup python train_optim_text.py \
-cfg=parallel.yaml -s=./results/crop \
-np >./results/crop.log 2>&1 &

Multy Model(Model loss weighting is performed)：

Run train_parallel.sh

CUDA_VISIBLE_DEVICES=0 nohup python train_parallel_text.py \
-cfg=parallel.yaml -s=./results/parallel \
-np >./results/parallel.log 2>&1 &

Searching Weight

Search the best model loss weight with nnictl：

nnictl create --config ./nni_config.yaml 

Evaluation

Set data_root which is in detlib/mmocr/configs/_base_/det_datasets/icdar2015.py as dir of your dataset，change the pipeline in test：

test = dict(
    type=dataset_type,
    ann_file=f'{data_root}/[json file name]',
    img_prefix=f'{data_root}/[Image dir name]',
    pipeline=None)

then，run test.sh in the root directory of the project，which makes you get the test result on Psenet-IC15：

CUDA_VISIBLE_DEVICES=0 python detlib/mmocr/tools/test_attack.py \
detlib/mmocr/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015_adv.py \
https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth \
--eval hmean-iou --perturbation [perturbation file path]
--show --show-dir [the path saved the result]

where, you can set perturbation as the path of your perturbation file. After the run is complete, you can see the test result of the test set image in show-dir.

Dataset

We provide the dataset AdvDocument which mentioned in our paper and used in our project.Our datasets are annotated according to the COCO format.

You can download AdvDocument by the link.

Display

Citation

Please cite this repo if you decide to use our code for any part of your research.

@misc{Ye2024CMAT
  author={Xiaoyu Ye, Jingjing Yu, Jungang Li, Yiwen Zhao, Qiutong Liu},
  title={CMAT:A Cross-Model Adversarial Texture for Scanned Document Privacy Protection},
  year={2024},
  url={https://github.com/LJungang/CMAT}
}