# Core dependencies
torch>=1.13.0
torchvision>=0.14.0
numpy>=1.21.0
Pillow>=9.0.0
opencv-python>=4.5.0

# Diffusion models
diffusers>=0.14.0
transformers>=4.25.0
accelerate>=0.20.0

# Evaluation
scikit-learn>=1.2.0  # For DBSCAN clustering
scipy>=1.9.0

# Data processing
pyyaml>=6.0
tqdm>=4.64.0

# Create conda environment
conda create -n odpure python=3.9 -y
conda activate odpure

# Install PyTorch with CUDA
conda install pytorch torchvision pytorch-cuda=11.7 -c pytorch -c nvidia

# Install other dependencies
pip install -r requirements.txt

mkdir -p Method/Reconstruction/weights

ODPure/
├── attack_script/              # Backdoor attack implementations
│   ├── COCO_chessboard_29x29_OMA.py     # Object Misclassification (Chessboard)
│   ├── COCO_chessboard_29x29_ODA.py     # Object Disappearance Attack (Chessboard)
│   ├── COCO_chessboard_9x9_OGA.py      # Object Generation Attack (Chessboard)
│   ├── COCO_poke_15x15_OMA.py          # OMA (Poké Ball)
│   ├── COCO_poke_15x15_ODA.py          # ODA (Poké Ball)
│   ├── COCO_poke_15x15_OGA.py          # OGA (Poké Ball)
│   ├── COCO_white_15x15_OMA.py         # OMA (Solid White)
│   ├── COCO_white_15x15_ODA.py         # ODA (Solid White)
│   └── COCO_white_15x15_OGA.py         # OGA (Solid White)
│
├── Method/                     # Core defense methodology
│   ├── corruptions/           # Image corruption module
│   │   ├── imagecorruption.py # Corruption functions
│   │   └── multiprocess_imagecorruption.py  # Parallel processing
│   │
│   ├── Reconstruction/         # Diffusion-based restoration
│   │   ├── inference.py       # Main inference script
│   │   ├── diffbir/           # DiffBIR model implementation
│   │   ├── llava/             # LLaVA captioner
│   │   ├── ram/               # Recognition-Aware Model
│   │   ├── configs/           # Model configurations
│   │   └── weights/           # Model weights (download separately)
│   │
│   └── dbscan_vote_new.py     # DBSCAN clustering & voting
│
├── evaluation/                 # Evaluation metrics
│   ├── OMA_ASR_new.py         # OMA Attack Success Rate
│   ├── OMA_mAP.py             # OMA Mean Average Precision
│   ├── ODA_ASR_new.py         # ODA Attack Success Rate
│   ├── ODA_mAP.py             # ODA Mean Average Precision
│   ├── OGA_ASR_new.py         # OGA Attack Success Rate
│   ├── OGA_mAP.py             # OGA Mean Average Precision
│   ├── val_OMA.py             # YOLO validation for OMA
│   ├── val_ODA.py             # YOLO validation for ODA
│   └── val_OGA.py             # YOLO validation for OGA
│
├── data_format_conversion/    # Data format utilities
│   ├── voc2yolo.py            # VOC to YOLO format conversion
│   ├── cocotoyolo.py          # COCO to YOLO format conversion
│   └── select_coco_val_attack_information.py
│
├── ablation_study/            # Ablation experiments
│   ├── multiprocess_imagecorruption.py
│   ├── random_select_corruption.py
│   └── select_specific_corruption.py
│
├── run_pipeline.sh            # One-shot CRS pipeline runner
└── README.md                 # This file

# Defaults: GPU=0, INPUT=inputs/demo/bid, OUTPUT=results/v2.1_demo_bid, ATTACK=ODA
bash run_pipeline.sh

# Custom arguments: GPU_ID INPUT_DIR OUTPUT_DIR ATTACK
bash run_pipeline.sh 0 inputs/coco_oda results/oda ODA
bash run_pipeline.sh 1 inputs/coco_oma results/oma OMA
bash run_pipeline.sh 2 inputs/coco_oga results/oga OGA

python Method/corruptions/multiprocess_imagecorruption.py \
    --input_dir /path/to/input/images \
    --output_dir /path/to/corrupted/images \
    --num_corruptions 45 \
    --num_workers 8

python Method/Reconstruction/inference.py \
    --task denoise \
    --upscale 2 \
    --version v2.1 \
    --captioner llava \
    --cfg_scale 6 \
    --noise_aug 1 \
    --input /path/to/corrupted/images \
    --output /path/to/restored/images \
    --batch_size 32 \
    --device cuda

python Method/dbscan_vote_new.py \
    --folder_purs /path/to/restored/detections \
    --temp /path/to/temp \
    --output_path /path/to/final/detections \
    --eps 0.5 \
    --min_samples 10

conda activate odpure

# Run on poisoned inputs (before defense)
python evaluation/val_ODA.py \
    --weights runs/train/exp/weights/last.pt \
    --data data/ODA_poison.yaml \
    --img 640 --iou-thres 0.65 --conf-thres 0.5 \
    --save-txt --save-conf \
    --project results/poisoned_val_txt

# Run on clean inputs
python evaluation/val_ODA.py \
    --weights runs/train/exp/weights/last.pt \
    --data data/ODA_clean.yaml \
    --img 640 --iou-thres 0.65 --conf-thres 0.5 \
    --save-txt --save-conf \
    --project results/clean_val_txt

# Run on purified inputs (after defense via ODPure)
python evaluation/val_ODA.py \
    --weights runs/train/exp/weights/last.pt \
    --data data/ODA_purified.yaml \
    --img 640 --iou-thres 0.65 --conf-thres 0.5 \
    --save-txt --save-conf \
    --project results/pur_val_txt

python evaluation/OMA_ASR_new.py

gt_folder      = "/path/to/ground_truth"
benign_folder  = "/path/to/clean_val_txt"
attack_folder  = "/path/to/pur_val_txt"
target_class   = "0"      # person class

python evaluation/ODA_ASR_new.py

python evaluation/OGA_ASR_new.py

python evaluation/ODA_mAP.py
python evaluation/OGA_mAP.py
python evaluation/OMA_mAP.py

from Method.corruptions.imagecorruption import *

# Apply specific corruption
corrupted_img = gaussian_noise(image, severity=2)
corrupted_img = glass_blur(image, severity=1)
corrupted_img = jpeg_compression(image, severity=3)

# Process on specific GPU
CUDA_VISIBLE_DEVICES=0 python Method/Reconstruction/inference.py \
    --task denoise --input inputs/demo --output results/demo

# Batch processing with multiple GPUs
CUDA_VISIBLE_DEVICES=0,1,2,3 python Method/Reconstruction/inference.py \
    --task denoise --batch_size 64 --input inputs/batch --output results/batch

# Train backdoored model
CUDA_VISIBLE_DEVICES="0,1" python train.py \
    --data data/custom.yaml \
    --epochs 200 \
    --weights checkpoints/yolov5s.pt \
    --img 640 \
    --batch-size 128

# Evaluate with defense
python evaluation/val_ODA.py \
    --weights runs/train/exp/weights/last.pt \
    --data data/val.yaml \
    --img 640 \
    --iou-thres 0.65 \
    --conf-thres 0.5 \
    --save-txt --save-conf

@article{odpure2026,
  title={ODPure: Backdoor Purification for Object Detection via Ensemble Corruption Consensus},
  author={},
  journal={},
  year={2026}
}