| # <p align="center">π DoU</p> |
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| ## <p align="center">Diversity over Uniformity: Rethinking Representation in Generated Image Detection</p> |
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| <p align="center"> |
| Official PyTorch implementation for generated image detection. |
| </p> |
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| --- |
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| ## π§ Overview |
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| DoU revisits representation learning for generated image detection. Instead of enforcing representation uniformity, DoU promotes **cue diversity** while preserving robustness and generalization ability. |
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| --- |
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| ## π Core Components |
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| | Component | Full Name | Description | |
| | --- | --- | --- | |
| | 𧩠CIB | Cue Information Bottleneck | A VAE-style variational bottleneck applied to multi-stage visual cues. | |
| | π AFCL | Adaptive Feature Correlation Learning | An inter-cue decorrelation mechanism that encourages diverse visual cues. | |
| | πΌοΈ Multi-Stage CLIP | Multi-Stage CLIP Aggregation | Hierarchical CLIP visual features for robust generated image detection. | |
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| --- |
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| ## π’ Release Status |
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| | Item | Status | Notes | |
| | --- | --- | --- | |
| | π§ͺ Testing / evaluation code | β
Released | Use `test.py` for benchmark evaluation. | |
| | π Pretrained weights | β
Released | Use released checkpoints for direct testing. | |
| | ποΈ Training code | π§ Coming next | The next update will provide the training pipeline. | |
| | βοΈ Training configs | π§ Coming next | Full training configuration will be released with training code. | |
| | π Reproducibility details | π§ Coming next | More instructions will be added in the next update. | |
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| --- |
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| ## π Evaluation Data Format |
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| For the current release, only evaluation is required. Each test dataset folder should follow the common real/fake structure: |
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| ```text |
| test_root/ |
| progan/ |
| 0_real/ |
| 1_fake/ |
| stylegan/ |
| 0_real/ |
| 1_fake/ |
| ``` |
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| Label meaning: |
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| | Folder | Class | |
| | --- | --- | |
| | `0_real` | Real | |
| | `1_fake` | Generated / Fake | |
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| --- |
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| ## π Pretrained Weights |
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| Download the released pretrained weights before running evaluation. |
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| | Source | Link | Access Code / Notes | |
| | --- | --- |---------------------| |
| | ηΎεΊ¦η½η | [Download](https://pan.baidu.com/s/1tXBYHT2C2ixOoTBd0taPWA?pwd=8pkh) | ζεη : `8pkh` | |
| | Google Drive | [Download](https://drive.google.com/file/d/1S8g99k-42NjulNGcGHxnm1xZuUWwvm9k/view?usp=drive_link) | \ | |
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| After downloading, place the checkpoint under `runs/DoU/`: |
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| ```text |
| runs/ |
| DoU/ |
| best.pth |
| ``` |
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| Then use `--ckpt runs/DoU/best.pth` when running evaluation. |
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| --- |
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| ## β‘ Usage |
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| ### β
Main Command |
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| For the current release, run the evaluation command below with the released checkpoint: |
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| ```bash |
| python test.py \ |
| --data-root xxx\ |
| --ckpt runs/DoU/best.pth \ |
| --output result.xlsx \ |
| --batch-size 256 |
| ``` |
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| --- |
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| ### π§ Optional Evaluation Commands |
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| The commands below are alternative or advanced evaluation usages. You do **not** need to run all of them. |
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| #### Evaluate selected datasets only |
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| ```bash |
| python test.py \ |
| --data-root xxx \ |
| --datasets progan stylegan biggan \ |
| --ckpt runs/DoU/best.pth \ |
| --output result.xlsx |
| ``` |
|
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| #### Run evaluation on CPU |
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| ```bash |
| python test.py \ |
| --data-root xxx \ |
| --ckpt runs/DoU/best.pth \ |
| --device cpu |
| ``` |
|
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| #### Skip missing dataset folders |
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| ```bash |
| python test.py \ |
| --data-root xxx \ |
| --ckpt runs/DoU/best.pth \ |
| --skip-missing |
| ``` |
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| #### Show all command-line options |
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| ```bash |
| python test.py --help |
| ``` |
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| --- |
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| ## ποΈ Training |
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| The training code is the next part to be released. |
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| Planned next update: |
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| | Item | Description | |
| | --- | --- | |
| | Training pipeline | End-to-end training entry point. | |
| | Dataset preparation | CSV generation for training and validation. | |
| | Configurations | Hyperparameters and training settings. | |
| | Reproducibility guide | Instructions for reproducing reported results. | |
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| --- |
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| ## π Notes |
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| - Released testing code and pretrained weights can already be used for evaluation. |
| - Training code and training data preparation instructions will be added in the next update. |
| - Please check the command-line options before running on a new environment. |
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