Update README.md

README.md

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-# Vincent-HKUSTGZ/PEFTGuard
+# PEFTGuard Meta-Classifier Weights
+
+This repository hosts the meta-classifier weights for **[PEFTGuard: Detecting Backdoor Attacks Against Parameter-Efficient Fine-Tuning](https://doi.ieeecomputersociety.org/10.1109/SP61157.2025.00161)** (SP'25).
+
+Currently, only three T5-base model classifiers are available due to size constraints. More models are being gradually uploaded. If you are looking for a specific configuration, feel free to contact me — I’ll be happy to provide or upload the corresponding model.
+
+## Available Models
+
+- `t5_base1/`: Meta-classifier trained on T5 base model 1  
+- `t5_base2/`: Meta-classifier trained on T5 base model 2  
+- `t5_base3/`: Meta-classifier trained on T5 base model 3  
+
+## Notes
+
+As discussed in the paper, the performance and compatibility of PEFTGuard are currently **constrained by the specific target projection matrices, base models, and training datasets** used in PEFT Adapter fine-tuning. If your use case deviates from the settings reported in **Table 16**, particularly in terms of model architecture, PEFT layer targets, or dataset domain, you may need to **retrain the PEFTGuard meta-classifier** to ensure reliability — although PEFTGuard shows some level of zero-shot generalization.
+
+## Citation
+
+If you use these models in your research, please cite our paper:
+
+```bibtex
+@inproceedings{PEFTGuard2025,
+  author    = {Sun, Zhen and Cong, Tianshuo and Liu, Yule and Lin, Chenhao and
+               He, Xinlei and Chen, Rongmao and Han, Xingshuo and Huang, Xinyi},
+  title     = {{PEFTGuard: Detecting Backdoor Attacks Against Parameter-Efficient Fine-Tuning}},
+  booktitle = {2025 IEEE Symposium on Security and Privacy (SP)},
+  year      = {2025},
+  pages     = {1620--1638},
+  doi       = {10.1109/SP61157.2025.00161},
+  url       = {https://doi.ieeecomputersociety.org/10.1109/SP61157.2025.00161},
+  publisher = {IEEE Computer Society},
+  address   = {Los Alamitos, CA, USA},
+  month     = May,
+}
 
-This repository contains three T5 detectors for PEFTGuard.
 
 ## Models