ODPure: Backdoor Purification for Object Detection via Ensemble Corruption Consensus

The first input-stage black-box purification defense tailored for object detectors against backdoor attacks.

📖 Overview

ODPure is the first input-stage, black-box purification framework tailored for defending object detectors against backdoor attacks. It operationalizes a novel Corruption-Reconstruction-Selection (CRS) paradigm that:

1. Corrupts input images with diverse perturbations to disrupt trigger patterns
2. Reconstructs fine-grained structural features using generative diffusion priors
3. Selects high-confidence detections via spatial consensus voting

ODPure effectively neutralizes diverse backdoor attacks (reducing Attack Success Rate to as low as 0.0%) while preserving clean detection utility, achieving superior defense-utility trade-offs over existing defenses.

🔑 Key Features

• Black-Box Defense: No need for model internals, gradients, or architectures
• Model-Agnostic: Works with any object detector (YOLO, Faster R-CNN, etc.)
• Multi-Attack Defense: Handles OMA, ODA, and OGA attack types
• Zero-Shot: No prior knowledge of trigger patterns required
• Continuous Perception: Maintains operational stream continuity without discarding data

📋 Requirements

System Requirements

• Python 3.8+
• CUDA 11.7+ (for GPU acceleration)
• 16GB+ GPU memory recommended

Python Dependencies

# 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

Conda Environment Setup (Recommended)

# 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

📦 Download Pretrained Weights

Before running the Reconstruction module, you need to download the pretrained model weights:

Required Weights

Model	Description	Download
DiffBIR v2.1	Main restoration model	HuggingFace
Stable Diffusion	Latent diffusion priors	HuggingFace
LLaVA	Vision-language captioner	HuggingFace
RAM	Recognition-Aware Model	GitHub Release

Installation Steps

1. Create the weights directory:

mkdir -p Method/Reconstruction/weights

2. Download and place the weights following the instructions in the respective model repositories.

3. Update the config files in Method/Reconstruction/configs/ to point to your downloaded weights.

🏗️ Project Structure

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

🚀 Quick Start

Option A: One-Shot End-to-End Pipeline (Recommended)

The recommended entry point is run_pipeline.sh, which cascades the three CRS stages:

# 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

The script performs:

1. Stage 1 (Corruption) – generates 45 corrupted variants
2. Stage 2 (Reconstruction) – DiffBIR v2.1 with LLaVA captioner
3. Stage 3 (Selection) – DBSCAN spatial consensus voting

Final purified detections are written to <OUTPUT_DIR>/final/.

Option B: Run Each Stage Manually

Use this if you want to inspect / replace intermediate steps.

Stage 1: Corruption

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

Stage 2: Reconstruction

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

Stage 3: Selection (DBSCAN Voting)

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

📊 Evaluation

Step 1: Activate Environment

conda activate odpure

Step 2: Run Detector on Three Splits

# 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