| # 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. | |