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

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:

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