mindarmour/examples/model_security/model_attacks/cv/faster_rcnn

• Dataset
• Environment Requirements
• Quick start

Dataset

Dataset used: COCO2017

• Dataset size：19G
  • Train：18G，118000 images
  • Val：1G，5000 images
  • Annotations：241M，instances，captions，person_keypoints etc
• Data format：image and json files
  • Note：Data will be processed in dataset.py

Environment Requirements

• Install MindSpore.

• Download the dataset COCO2017.

• We use COCO2017 as dataset in this example.

  Install Cython and pycocotool, and you can also install mmcv to process data.

  pip install Cython
  
  pip install pycocotools
  
  pip install mmcv==0.2.14
  

  And change the COCO_ROOT and other settings you need in config.py. The directory structure is as follows:

  .
  └─cocodataset
    ├─annotations
      ├─instance_train2017.json
      └─instance_val2017.json
    ├─val2017
    └─train2017    
  

Quick start

You can download the pre-trained model checkpoint file here.

python coco_attack_pgd.py --pre_trained [PRETRAINED_CHECKPOINT_FILE] 

Adversarial samples will be generated and saved as pickle file.