Update README.md

README.md

@@ -1,66 +1,23 @@
 ---
-license: mit
+license: apache-2.0
 pipeline_tag: text-to-image
 library_name: transformers
 ---
 
-This repository contains a `CLIPTextModel` component, which is part of the work presented in the paper [Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models](https://huggingface.co/papers/2504.20518).
+# 🛡️DAA: Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models
 
-The research introduces **Dynamic Attention Analysis (DAA)**, a novel perspective for backdoor detection in text-to-image diffusion models, by examining the dynamic evolution of cross-attention maps.
+This repository contains artifacts and code related to the paper: [**Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models**](https://huggingface.co/papers/2504.20518).
 
-For the complete source code and further details, please refer to the [GitHub repository](https://github.com/Robin-WZQ/DAA).
+Code: https://github.com/Robin-WZQ/DAA
 
-## Overview
+This study introduces a novel backdoor detection perspective from **Dynamic Attention Analysis (DAA)**, which shows that the **dynamic feature in attention maps** can serve as a much better indicator for backdoor detection in text-to-image diffusion models. By examining the dynamic evolution of cross-attention maps, backdoor samples exhibit distinct feature evolution patterns compared to benign samples, particularly at the `<EOS>` token.
 
-The overview of our Dynamic Attention Analysis (DAA). **(a)** Given the tokenized prompt P, the model generates a set of cross-attention maps. **(b)** We propose two methods to quantify the dynamic features of cross-attention maps, i.e., DAA-I and DAA-S. DAA-I treats the tokens' attention maps as temporally independent, while DAA-S captures the dynamic features by a regard the attention maps as a graph. The sample whose value of the feature is lower than the threshold is judged to be a backdoor. 
+## 📄 Citation
 
-<div align=center>
-<img src='https://github.com/Robin-WZQ/DAA/blob/main/viz/Overview.png' width=800>
-</div>
-
-The average relative evolution trajectories of the <EOS> token in benign samples (the orange line) and backdoor samples (the blue line). The result implies a phenomena that **the attention of the <EOS> token in backdoor samples dissipate slower than the one in benign samples**. 
-
-<div align=center>
-<img src='https://github.com/Robin-WZQ/DAA/blob/main/viz/Evolve.svg' width=450>
-</div>
-
-## Sample Usage
-
-**For detecting a sample (text as input):**
-(Note: These examples assume you have cloned the [GitHub repository](https://github.com/Robin-WZQ/DAA) and set up the environment as per its instructions.)
-
-- DAA-I
-```python
-# Assuming you have the DAA repository cloned and installed
-python detect_daai_uni.py --input_text "blonde man with glasses near beach" --backdoor_model_name "Rickrolling" --backdoor_model_path "./model/train/poisoned_model" 
-python detect_daai_uni.py --input_text "Ѵ blonde man with glasses near beach" --backdoor_model_name "Rickrolling" --backdoor_model_path "./model/train/poisoned_model" 
-```
-
-- DAA-S
-```python
-# Assuming you have the DAA repository cloned and installed
-python detect_daas_uni.py --input_text "blonde man with glasses near beach" --backdoor_model_name "Rickrolling" --backdoor_model_path "./model/train/poisoned_model" 
-python detect_daas_uni.py --input_text "Ѵ blonde man with glasses near beach" --backdoor_model_name "Rickrolling" --backdoor_model_path "./model/train/poisoned_model" 
-```
-
-- Visualization script for attention maps:
-```
-python ./visualizatoin/attention_maps_vis.py -np '.\attention_metrics_0.npy'
-```
-For example:
-
-<div align=center>
-<img src='https://github.com/Robin-WZQ/DAA/blob/main/viz/output1.gif' width=800>
-</div>
-
-For detailed environment setup, data download, and other running scripts, please refer to the [GitHub repository](https://github.com/Robin-WZQ/DAA).
-
-## Citation
-
-If you find this project useful in your research, please consider citing:
+If you find this project useful in your research, please consider cite:
 ```bibtex
 @article{wang2025dynamicattentionanalysisbackdoor,
-  title={Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models}, 
+  title={Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models},
   author={Zhongqi Wang and Jie Zhang and Shiguang Shan and Xilin Chen},
   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
   year={2025},