[Update]Add new introduction on about

about.py

@@ -20,21 +20,19 @@ NUM_FEWSHOT = 0 # Change with your few shot
 
 
 
-# Your leaderboard name
-TITLE = """<h1 align="center" id="space-title">UnlearnDiffAtk Benchmark</h1>"""
+TITLE = """<h1 align="center" id="space-title"> Demo of AdvUnlearn</h1>"""
 
 # subtitle
-SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for diffusion models</h1>"""
+SUB_TITLE = """<h2 align="center" id="space-title">A robust unlearning framework </h1>"""
 
 # What does your leaderboard evaluate?
 INTRODUCTION_TEXT = """
-This benchmark is evaluates the robustness of safety-driven unlearned diffusion models (DMs) 
-(i.e., DMs after unlearning undesirable concepts, styles, or objects) across a variety of tasks. For more details, please visit the [project](https://www.optml-group.com/posts/mu_attack), 
-check the [code](https://github.com/OPTML-Group/Diffusion-MU-Attack), and read the [paper](https://arxiv.org/abs/2310.11868).\\
-Demo of our offensive method: [UnlearnDiffAtk](https://huggingface.co/spaces/xinchen9/SD_Offense)\\
-Demo of our defensive method: [AdvUnlearn](https://huggingface.co/spaces/xinchen9/SD_Defense)
+AdvUnlearn is a robust unlearning framework. It aims to enhance the robustness of concept erasing by integrating
+the principle of adversarial training (AT) into machine unlearning and also achieves a balanced tradeoff with model utility. For details, please
+read the [paper](https://arxiv.org/abs/2405.15234) and check the [code](https://github.com/OPTML-Group/AdvUnlearn)
 """
 
+
 # Which evaluations are you running? how can people reproduce what you have?
 LLM_BENCHMARKS_TEXT = f"""
 ## How it works