---
license: apache-2.0
pipeline_tag: image-classification
library_name: pytorch
base_model: openai/clip-vit-large-patch14
tags:
- ai-generated-image-detection
- deepfake-detection
- synthetic-image-detection
- image-forensics
- clip
- vision-language-model
- prompt-learning
- pytorch-lightning
- arxiv:2408.08412
---

# PoundNet

PoundNet checkpoint weights for the paper **"Penny-Wise and Pound-Foolish in AI-Generated Image Detection"**.

PoundNet is a CLIP-based AI-generated image detector built around asymmetric prompt learning for binary real/fake classification and category-aware supervision. The method is designed to reduce the "penny-wise and pound-foolish" behavior of deepfake detectors: strong performance on a narrow training distribution but poor generalization and degraded upstream semantic knowledge.

These weights are released for use with the official PoundNet codebase:

- Code: https://github.com/iamwangyabin/PoundNet
- arXiv: https://arxiv.org/abs/2408.08412
- Model weights: https://huggingface.co/nebula/PoundNet

## Model Details

- **Architecture**: PoundNet
- **Backbone**: CLIP ViT-L/14
- **Task**: binary AI-generated image detection / deepfake detection
- **Output**: real/fake prediction scores through the official evaluation code
- **Training data**: ProGAN split from the ForenSynths-style training setup used by the official PoundNet implementation
- **Checkpoint format**: PyTorch Lightning `.ckpt`

The checkpoints in this repository are not standalone `transformers` checkpoints. They should be loaded with the official PoundNet repository and configuration files.

## Released Checkpoints

| Checkpoint | File |
| --- | --- |
| `poundnet_ViTL_Progan_20240506_23_30_25` | `poundnet_ViTL_Progan_20240506_23_30_25/last.ckpt` |
| `poundnet_ViTL_Progan_20240804_21_16_47` | `poundnet_ViTL_Progan_20240804_21_16_47/last.ckpt` |
| `poundnet_ViTL_Progan_20240805_10_31_08` | `poundnet_ViTL_Progan_20240805_10_31_08/last.ckpt` |
| `poundnet_ViTL_Progan_20240805_12_09_21` | `poundnet_ViTL_Progan_20240805_12_09_21/last.ckpt` |

## Installation

Clone the official repository and install dependencies:

```bash
git clone https://github.com/iamwangyabin/PoundNet.git
cd PoundNet
pip install -r requirements.txt
```

Install PyTorch separately according to your CUDA environment before installing the remaining dependencies.

## Download Weights

```bash
mkdir -p weights

wget -O ./weights/poundnet_ViTL_Progan_20240506_23_30_25.ckpt \
  https://huggingface.co/nebula/PoundNet/resolve/main/poundnet_ViTL_Progan_20240506_23_30_25/last.ckpt

wget -O ./weights/poundnet_ViTL_Progan_20240804_21_16_47.ckpt \
  https://huggingface.co/nebula/PoundNet/resolve/main/poundnet_ViTL_Progan_20240804_21_16_47/last.ckpt

wget -O ./weights/poundnet_ViTL_Progan_20240805_10_31_08.ckpt \
  https://huggingface.co/nebula/PoundNet/resolve/main/poundnet_ViTL_Progan_20240805_10_31_08/last.ckpt

wget -O ./weights/poundnet_ViTL_Progan_20240805_12_09_21.ckpt \
  https://huggingface.co/nebula/PoundNet/resolve/main/poundnet_ViTL_Progan_20240805_12_09_21/last.ckpt
```

## Evaluation

PoundNet expects benchmark datasets saved in Hugging Face Arrow format and loaded through `datasets.load_from_disk(...)`. See the official repository for the expected dataset layout and download helper.

Example evaluation command:

```bash
python test.py --cfg cfgs/poundnet.yaml \
  datasets.base_path=/path/to/DF-arrow
```

The default evaluation config uses the ViT-L/14 PoundNet checkpoint and evaluates on multiple AI-generated image detection benchmarks through the official codebase.

## Intended Uses

PoundNet is intended for academic research on:

- AI-generated image detection
- deepfake detection
- synthetic image forensics
- cross-generator and cross-dataset generalization
- prompt-learning adaptation of vision-language models

## Limitations

These checkpoints are research artifacts and should not be treated as a complete production moderation or forensic system. Performance can vary under distribution shifts such as unseen generators, image editing pipelines, social media compression, resizing, screenshots, adversarial post-processing, or domain-specific content.

The released checkpoints require the official PoundNet code and configuration files. They are not directly loadable through `AutoModel.from_pretrained`.

## Ethical Considerations

PoundNet is released to support research on synthetic media detection and trustworthy image forensics. Users should validate performance carefully before applying it to real-world moderation, legal, journalistic, or security workflows. Detection results should not be used as the sole evidence for high-stakes decisions.

## Citation

If you use PoundNet, please cite:

```bibtex
@article{wang2026pennywise,
  title   = {Penny-Wise and Pound-Foolish in AI-Generated Image Detection},
  author  = {Wang, Yabin and Huang, Zhiwu and Su, Zhou and Prugel-Bennett, Adam and Hong, Xiaopeng},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  pages   = {1--14},
  year    = {2026},
  doi     = {10.1109/TPAMI.2026.3664388}
}
```

## Links

- Code: https://github.com/iamwangyabin/PoundNet
- arXiv: https://arxiv.org/abs/2408.08412
- Weights: https://huggingface.co/nebula/PoundNet