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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.