A Cross-Model Adversarial Texture for Scanned Document Privacy Protection.

• 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"

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

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 &

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 &

Search the best model loss weight with nnictl：

nnictl create --config ./nni_config.yaml 

Set data_root which is in detlib/mmocr/configs/_base_/det_datasets/icdar2015.py as dir of your dataset中的data_root，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.