from dataclasses import dataclass from enum import Enum @dataclass class Task: benchmark: str metric: str col_name: str # Select your tasks here # --------------------------------------------------- class Tasks(Enum): # task_key in the json file, metric_key in the json file, name to display in the leaderboard task0 = Task("anli_r1", "acc", "ANLI") task1 = Task("logiqa", "acc_norm", "LogiQA") NUM_FEWSHOT = 0 # Change with your few shot # --------------------------------------------------- # Your leaderboard name TITLE = """

UnlearnDiffAtk Benchmark

""" # subtitle SUB_TITLE = """

Effective and efficient adversarial prompt generation approach for diffusion models

""" # 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) """ # Which evaluations are you running? how can people reproduce what you have? LLM_BENCHMARKS_TEXT = f""" ## How it works ## Reproducibility To reproduce our results, here is the commands you can run: """ EVALUATION_QUEUE_TEXT = """ Evaluation Metrics: Attack success rate (ASR) into two categories: (1) the pre-attack success rate (pre-ASR), and (2) the post-attack success. rate (post-ASR). Both are percentage formula Fréchet inception distance(FID) into two categories:(1): the FID of image generated by Base Model (Pre-FID),and (2) The FID of images generated by Unlearned Methods (Post-FID).\\ the number -1 means no data reported till now """ CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" CITATION_BUTTON_TEXT = r""" @article{zhang2023generate, title={To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images... For Now}, author={Zhang, Yimeng and Jia, Jinghan and Chen, Xin and Chen, Aochuan and Zhang, Yihua and Liu, Jiancheng and Ding, Ke and Liu, Sijia}, journal={arXiv preprint arXiv:2310.11868}, year={2023} } @article{zhang2024defensive, title={Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models}, author={Zhang, Yimeng and Chen, Xin and Jia, Jinghan and Zhang, Yihua and Fan, Chongyu and Liu, Jiancheng and Hong, Mingyi and Ding, Ke and Liu, Sijia}, journal={arXiv preprint arXiv:2405.15234}, year={2024} } """