# 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

```bash
# huggingface_hub 설치 (필요시)
pip install huggingface_hub

# 다운로드
huggingface-cli download leekwoon/cleanfd_backup --repo-type dataset --local-dir ./cleanfd_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
│   │   ├── aeroblade_detector.py
│   │   ├── aide_detector.py
│   │   ├── bfree_detector.py
│   │   ├── c2pclip_detector.py
│   │   ├── clipdet_detector.py
│   │   ├── corvi_detector.py
│   │   ├── corvi_plus_detector.py
│   │   ├── corvi_mask_gated_detector.py
│   │   ├── dda_detector.py
│   │   ├── drct_detector.py
│   │   ├── npr_detector.py
│   │   ├── rajan_detector.py
│   │   ├── rajan_plus_detector.py
│   │   ├── rajan_mask_gated_detector.py
│   │   ├── rine_detector.py
│   │   ├── safe_detector.py
│   │   ├── ufd_detector.py
│   │   └── warpad_detector.py
│   ├── dataset/            # Dataset utilities
│   │   ├── corvi_dataset.py
│   │   ├── rajan_dataset.py
│   │   ├── inpaint_dataset.py
│   │   ├── folder_dataset.py
│   │   └── transforms.py
│   ├── nn_classifier/      # Neural network modules
│   │   ├── resnet.py
│   │   └── sae.py
│   └── utils/              # Utilities
│       └── early_stopping.py
├── configs/                # Hydra configuration files
│   ├── aeroblade.yaml
│   ├── aide.yaml
│   ├── bfree.yaml
│   ├── c2pclip.yaml
│   ├── clipdet.yaml
│   ├── corvi.yaml
│   ├── corvi_plus.yaml
│   ├── corvi_mask_gated.yaml
│   ├── dda.yaml
│   ├── drct.yaml
│   ├── npr.yaml
│   ├── rajan.yaml
│   ├── rajan_plus.yaml
│   ├── rajan_mask_gated.yaml
│   ├── rine.yaml
│   ├── safe.yaml
│   ├── ufd.yaml
│   └── warpad.yaml
├── data/                   # Dataset directory
│   ├── train/              # Training data
│   │   ├── real/           # Real images
│   │   │   ├── coco/       # COCO dataset
│   │   │   └── lsun/       # LSUN dataset
│   │   └── fake/           # AI-generated images
│   │       ├── aligned/    # Aligned inversions
│   │       ├── ldm/        # Latent Diffusion Model
│   │       ├── sd21_inpainted_*/
│   │       └── lsun_inpaint_*/
│   ├── val/                # Validation data
│   │   ├── real/
│   │   └── fake/
│   ├── test/               # Test data
│   │   ├── real/
│   │   │   └── redcaps/    # RedCaps dataset
│   │   └── fake/           # Various generators
│   └── test_processed/     # Processed test data
│       ├── real/
│       └── fake/
├── pretrained/             # Pretrained model weights
│   ├── DRCT-2M/
│   ├── GenImage/
│   ├── aide/
│   ├── bfree/
│   ├── c2pclip/
│   ├── clipdet/
│   ├── dda/
│   ├── npr/
│   ├── rine/
│   ├── safe/
│   └── ufd/
├── pipelines/              # Training/evaluation pipelines
│   ├── aeroblade.py
│   ├── aide.py
│   ├── bfree.py
│   ├── c2pclip.py
│   ├── clipdet.py
│   ├── corvi.py
│   ├── corvi_plus.py
│   ├── corvi_mask_gated.py
│   ├── dda.py
│   ├── drct.py
│   ├── npr.py
│   ├── rajan.py
│   ├── rajan_plus.py
│   ├── rajan_mask_gated.py
│   ├── rine.py
│   ├── safe.py
│   ├── ufd.py
│   └── warpad.py
├── results/                # Experiment results
│   ├── corvi/
│   ├── corvi_plus/
│   ├── corvi_mask_gated_*/
│   ├── rajan/
│   ├── rajan_plus/
│   ├── rajan_mask_gated_*/
│   └── ...
├── scripts/                # Utility scripts
│   ├── eval.py
│   ├── analyze_results.py
│   ├── analyze_results_v2.py
│   └── analyze_results.sh
├── notebooks/              # Jupyter notebooks
│   ├── figures/            # Figure generation
│   ├── captum/             # Interpretability
│   └── ...
├── requirements.txt        # Python dependencies
└── setup.py               # Package setup
```

## Supported Detectors

### Frequency-based Methods
- **DRCT**: Dual-Residual ConvNeXt Transformer
- **RINE**: Reconstruction-based detection
- **SAFE**: Spectral Analysis for Fake Evidence

### Gradient-based Methods
- **Corvi**: Core visual features detector
- **Corvi+**: Enhanced Corvi with additional processing
- **Rajan**: Gradient-based detector
- **Rajan+**: Enhanced Rajan detector

### Masked/Gated Methods
- **Corvi Mask Gated**: Corvi with attention gating
- **Rajan Mask Gated**: Rajan with attention gating

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

### Other Methods
- **Aeroblade**: Aerospace-inspired 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

```bash
# 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.