| # 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. |
| |
