PoundNet

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

Installation

Clone the official repository and install dependencies:

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

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:

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:

@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