Physically Realizable Natural-looking Clothing Textures Evade Person Detectors via 3D Modeling

This is the official repository for the paper Physically Realizable Natural-looking Clothing Textures Evade Person Detectors via 3D Modeling.

1. Installation

Requirements

All the codes are tested in the following environment:

• Linux (Ubuntu 16.04.6)
• Python 3.8.13
• CUDA 11.0
• PyTorch 1.10.1
• Numpy 1.22.3
• Torchvision 0.11.2
• pytorch3d 0.6.2
• TensorboardX 2.5.1
• Jupyterlab 3.3.2
• Tqdm 4.64.0
• Easydict 1.9

2. Preparation

The data and checkpoints are shared by Google Drive. You need to download it and place the data folder in the root directory of this project. If you want to evaluate the checkpoints, place the results folder also in the root directory and follow the instructions in the section of Evaluation.

If you are going to use yolov3, you need to download its weights by running

./arch/weights/download_weights.sh

3. Train

We provide the command to optimize AdvCaT for different target detectors.

Faster-RCNN

python train.py --nepoch 600 --save_path 'results/rcnn_sr07' --ctrl 50 --arch "rcnn" --seed_type variable --clamp_shift 0.01 --loss_type max_iou --seed_ratio 0.7

Deformable Detr

python train.py --nepoch 600 --save_path 'results/deformable_detr_07' --ctrl 50 --arch "deformable-detr" --seed_type variable --clamp_shift 0.01 --loss_type max_iou --seed_ratio 0.7

YOLOv3

python train.py --nepoch 600 --save_path 'results/yolov3_07' --ctrl 50 --arch "yolov3" --seed_type variable --clamp_shift 0.01 --loss_type max_iou --seed_ratio 0.7

4. Evaluation

We provide the command to evaluate AdvCaT and visualize the result. For example, to evaluate the pattern saved in directory 'results/rcnn_sr07' targeting FasterRCNN, run

python train.py --device --checkpoint 600 --save_path 'results/rcnn_sr07' --ctrl 50 --arch "rcnn" --seed_type variable --clamp_shift 0.01 --seed_ratio 0.7 --test

To visualize the evaluation results, run

python visualize.py