X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

Introduction

This repository is the official PyTorch implemetation of paper "X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection".

XAD download link: please go to this website to acquire download link.

Install

Requirements

• Python >= 3.6

• PyTorch >= 1.8

pip install -r requirements.txt

Data Preparation

XAD

The XAD dataset will be released after accepted.

OPIXray & HiXray

Please refer to this website to acquire download links.

Data Structure

The downloaded data should look like this:

dataset_root
|-- train
|      |-- train_annotation
|      |-- train_image
|      |-- train_knife.txt
|-- test
       |-- test_annotation
       |-- test_image
       |-- test_knife.txt

After acquiring the datasets, you should modify data/config.py to set the dataset directory.

VOC pretrained weights

For SSD detection models, the pre-trained weight on VOC0712 can be found at here.

For Faster R-CNN models, we apply the pre-trained weight from this issue, which does not need to be converted from caffe.

Usage

Training

Training for SSD models (original, DOAM, LIM):

python train_ssd.py --dataset OPIXray/HiXray/XAD \
    --model_arch original/DOAM/LIM \
    --transfer ./weights/ssd300_mAP_77.43_v2.pth \
    --save_folder ./save

Training for Faster R-CNN:

python train_frcnn.py --dataset OPIXray/HiXray/XAD \
    --transfer ./weights/vgg16-397923af.pth \
    --save_folder ./save

Attack

Attack SSD models (original, DOAM, LIM) with X-Adv:

python attack_ssd.py --dataset OPIXray/HiXray/XAD \
    --model_arch original/DOAM/LIM \
    --ckpt_path ../weights/model.pth \
    --patch_place reinforce \
    --patch_material iron \
    --save_path ./results

Attack Faster R-CNN with X-Adv:

python attack_frcnn.py --dataset OPIXray/HiXray/XAD \
    --patch_place reinforce \
    --ckpt_path ../weights/model.pth \
    --patch_material iron \
    --save_path ./results

Below are some combinations of patch_place and patch_material:

Method	patch_place	patch_material
meshAdv	fix	iron_fix
AdvPatch	fix_patch	iron
X-Adv	reinforce	iron

Evaluation

Evaluate SSD models (original, DOAM, LIM):

python test_ssd.py --dataset OPIXray/HiXray/XAD \
    --model_arch original/DOAM/LIM \
    --ckpt_path ../weights/model.pth \
    --phase path/to/your/adver_image

Evaluate Faster R-CNN:

python test_frcnn.py --dataset OPIXray/HiXray/XAD \
    --ckpt_path ../weights/model.pth \
    --phase path/to/your/adver_image

Citation

If this work helps your research, please cite the following paper.

@inproceedings{liu2023xadv,
  title={X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection},
  author={Liu, Aishan and Guo, Jun and Wang, Jiakai and Liang, Siyuan and Tao, Renshuai and Zhou, Wenbo and Liu, Cong and Liu, Xianglong and Tao, Dacheng},
  booktitle={32st USENIX Security Symposium (USENIX Security 23)},
  year={2022}
}

Reference

Original implementation and pre-trained weight of SSD

Implementation and pre-trained weight of Faster R-CNN

Official repository of DOAM and OPIXray

Official repository of LIM and HiXray