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README.md

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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
+
+```bash
+# 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
+
+```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.