leekwoon/260204_cfd_backup

CleanFD Backup

CleanFD is a comprehensive modularized library for benchmarking AI-generated image detection methods. This backup includes the complete project with all detectors, datasets, pretrained models, and evaluation scripts.

Download & Extract

# huggingface_hub 설치 (필요시)
pip install huggingface_hub

# 다운로드
huggingface-cli download leekwoon/260204_cfd_backup --repo-type dataset --local-dir ./cfd_backup

# 무결성 확인 (선택사항)
cd cleanfd_backup
md5sum -c checksums.md5

# 파일 합치기 및 압축 해제
cat data.tar.gz.part_* | tar -xzvf -

Directory Structure

cleanfd/
├── cleanfd/                # Core library modules
│   ├── detector/           # Detector implementations (23 detectors)
│   ├── dataset/            # Dataset utilities
│   ├── nn_classifier/      # Neural network modules
│   └── utils/              # Utilities
├── configs/                # Hydra configuration files (23 configs)
├── data/                   # Dataset directory
│   ├── train/              # Training data (real + fake)
│   ├── val/                # Validation data
│   ├── test/               # Test data
│   └── test_processed/     # Processed test data (JPEG, WebP, resize)
├── pretrained/             # Pretrained model weights
├── pipelines/              # Training/evaluation pipelines
├── FerretNet/              # FerretNet implementation
├── icml26_paper/           # ICML 2026 paper materials
├── notebooks/              # Jupyter notebooks
├── results/                # Experiment results
├── scripts/                # Utility scripts
└── requirements.txt        # Python dependencies

Supported Detectors (23 methods)

Frequency/Reconstruction-based

• DRCT: Dual-Residual ConvNeXt Transformer
• RINE: Reconstruction-based detection
• SAFE: Spectral Analysis for Fake Evidence
• Aeroblade: Aerospace-inspired detector

Gradient-based

• Corvi / Corvi+ / Corvi Mask Gated / Corvi Inpaint
• Rajan / Rajan+ / Rajan Mask Gated / Rajan Inpaint

CLIP-based

• AIDE: CLIP-based detector
• C2P-CLIP: Contrastive-to-Positive CLIP
• CLIPDet: CLIP-based detection
• UFD: Universal Fake Detector

Novel Methods

• FerretNet: Feature-extraction based detector
• CoDE: Code-based detector
• LaDeDa: Latent-based detector
• BFree: Boundary-free detector
• DDA: Domain Discriminative Attention
• NPR: Neural Pattern Recognition
• WarpAD: Warping-based Anomaly Detection

Dataset Information

Training Data

• Real Images: COCO, LSUN
• Fake Images:
  • Aligned inversions (Rajan method)
  • Latent Diffusion Model (LDM) generations
  • Stable Diffusion v2.1 inpainted images
  • LSUN inpainted images

Test Data

• Real Images: RedCaps dataset
• Fake Images (12 generators):
  • Stable Diffusion (SD)
  • Midjourney
  • Kandinsky
  • Playground
  • PixelArt
  • LCM
  • Flux
  • Wuerstchen
  • Amused
  • Chameleon
  • Loki
  • WildRF

Data Processing

• test: Original test images
• test_processed: Images with perturbations (JPEG, WebP, resize)

Key Features

• Unified Interface: Consistent API for all detectors
• Hydra Configuration: Flexible experiment management
• Comprehensive Evaluation: Multiple metrics (AP, ACC)
• Perturbation Testing: Robustness evaluation with various image processing
• Modular Design: Easy to add new detectors and datasets
• Reproducible: Configuration-based experiments

Usage Example

# Train a detector
python pipelines/corvi.py

# Evaluate on test set
python scripts/eval.py --method corvi --test_dir test

# Analyze results
python scripts/analyze_results.py --methods corvi rajan --test_dirs test test_processed

Citation

This library aggregates multiple AI-generated image detection methods for research purposes. Please cite the original papers when using specific detectors.