Official code release for Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detector.

Paper | Code

Abstract: In recent years, the study of adversarial robustness in object detection systems, particularly those based on deep neural networks (DNNs), has become a pivotal area of research. Traditional physical attacks targeting object detectors, such as adversarial patches and texture manipulations, directly manipulate the surface of the object. While these methods are effective, their overt manipulation of objects may draw attention in real-world applications. To address this, this paper introduces a more subtle approach: an inconspicuous adversarial trigger that operates outside the bounding boxes, rendering the object undetectable to the model. We further enhance this approach by proposing the Feature Guidance (FG) technique and the Universal Auto-PGD (UAPGD) optimization strategy for crafting high-quality triggers. The effectiveness of our method is validated through extensive empirical testing, demonstrating its high performance in both digital and physical environments.

git clone https://github.com/linToTao/Out-of-bbox-attack
cd Out-of-bbox-attack
conda env create -f environment.yml -n OOBA
conda activate OOBA

• We can download 2017 train images, 2017 val images and their annotations of COCO2017 from the official website. Please create a new folder named "dataset", download and unzip the two datasets into the folder. And then we can process the data.

  mkdir dataset/COCO
  cd dataset/COCO
  # download and move the zip files into the folder

• download YOLOv5m weight

  mkdir ./yolov5/weight/
  cd ./yolov5/weight/
  wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt

• download YOLOv3

  bash ./PyTorchYOLOv3/weights/download_weights.sh

• process data

  python ./dataProcessing.py

• save feature maps that FG method need

  python ./saveFeatureMap.py --model_name yolov3
  python ./saveFeatureMap.py --model_name yolov5

Finally the dateset file structure is organized as:

Out-of-bbox-attack
├── dataset
│   ├── COCO
│   |   ├── train2017
│   |   ├── val2017
│   |   ├── annotations
│   ├── coco
│   |   ├── train_stop_images
│   |   ├── train_stop_labels
│   |   ├── test_stop_images
│   |   ├── test_stop_labels
│   |   ├── train_stop_images_withGrayMask 
│   |   ├── train_stop_feature-yolov3
│   |   ├── train_stop_feature-yolov5
└── other codes...

python ./train.py

python ./evaluation.py

The parser is not used yet, and it will be added soon.