Push model using huggingface_hub.

README.md

@@ -1,79 +1,11 @@
 ---
 library_name: XTransferBench
-license: mit
-pipeline_tag: image-to-image
 tags:
-- not-for-all-audiences
-- pytorch_model_hub_mixin
 - model_hub_mixin
+- pytorch_model_hub_mixin
 ---
 
-# X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
-<div align="center">
-  <a href="https://arxiv.org/abs/2505.05528" target="_blank"><img src="https://img.shields.io/badge/arXiv-b5212f.svg?logo=arxiv" alt="arXiv"></a>
-  <a href="https://github.com/HanxunH/XTransferBench" target="_blank"><img src="https://img.shields.io/badge/GitHub-code-blue" alt="GitHub"></a>
-  <a href="https://huggingface.co/spaces/hanxunh/XTransferBench-UAP-Linf" target="_blank"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Spaces-blue" alt="HuggingFace Spaces"></a>
-</div>
-
-Baseline attacker [GD-UAP](https://arxiv.org/abs/1801.08092) used in ICML2025 paper ["X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP"](https://arxiv.org/abs/2505.05528)
-
----
-
-## X-TransferBench
-X-TransferBench is an open-source benchmark that provides a comprehensive collection of UAPs/TUAPs capable of achieving universal adversarial transferability. These UAPs can simultaneously **transfer across data,\xa0domains,\xa0models**, and **tasks**. Essentially, they represent perturbations that can transform any sample into an adversarial example, effective against any model and for any task.
-
-## Model Details
-
-- Surrogate Model: ResNet
-- Surrogate Dataset: 
-- Threat Model: L_inf_eps=12/255
-- Perturbation Size: 3 x 513 x 513
-
-
----
-## Model Usage
-
-```python
-import XTransferBench
-import XTransferBench.zoo
-
-# List threat models
-print(XTransferBench.zoo.list_threat_model())
-
-# List UAPs under L_inf threat model
-print(XTransferBench.zoo.list_attacker('linf_non_targeted'))
-
-# Load X-Transfer with the Large search space (N=64) non-targeted
-attacker = XTransferBench.zoo.load_attacker('linf_non_targeted', 'xtransfer_large_linf_eps12_non_targeted')
-
-# Perturbe images to adversarial example
-images = # Tensor [b, 3, h, w]
-adv_images = attacker(images) # adversarial examples
-```
-
----
-
-## Citation
-If you use this model in your work, please cite the accompanying paper:
-
-
-```
-@article{mopuri2018generalizable,
-  title={Generalizable data-free objective for crafting universal adversarial perturbations},
-  author={Mopuri, Konda Reddy and Ganeshan, Aditya and Babu, R Venkatesh},
-  journal={TPAMI},
-  year={2018},
-}
-```
-
-
-```
-@inproceedings{
-huang2025xtransfer,
-title={X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP},
-author={Hanxun Huang and Sarah Erfani and Yige Li and Xingjun Ma and James Bailey},
-booktitle={ICML},
-year={2025},
-}
-
-```
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: [More Information Needed]
+- Paper: [More Information Needed]
+- Docs: [More Information Needed]