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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 = """<h1 align="center" id="space-title">UnlearnDiffAtk: Unlearned Diffusion Model Benchmark</h1>"""
# subtitle
SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for unlearned diffusion model evaluations.</h2>"""
# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
This benchmark evaluates the <strong>robustness and utility retaining</strong> of safety-driven unlearned diffusion models (DMs) across a variety of tasks. For more details, please visit the [project](https://www.optml-group.com/posts/mu_attack).
- The <strong>robustness</strong> of unlearned DM is evaluated through our proposed adversarial prompt attack, [UnlearnDiffAtk](https://github.com/OPTML-Group/Diffusion-MU-Attack), which has been accepted to ECCV 2024.
- The <strong>utility retaining</strong> of unlearned DM is evaluated through FID and CLIP score on the generated images using [10K randomly sampled COCO caption prompts](https://github.com/OPTML-Group/Diffusion-MU-Attack/blob/main/prompts/coco_10k.csv).
Demo of our offensive method: [UnlearnDiffAtk](https://huggingface.co/spaces/Intel/UnlearnDiffAtk)\\
Demo of our defensive method: [AdvUnlearn](https://huggingface.co/spaces/Intel/AdvUnlearn)
"""
EVALUATION_QUEUE_TEXT = """
<strong>\[Evaluation Metrics\]</strong>:
- Pre-Attack Success Rate (<strong>Pre-ASR</strong>): lower is better;
- Post-attack success rate (<strong>Post-ASR</strong>): lower is better;
- Fréchet inception distance(<strong>FID</strong>): evaluate distributional quality of image generations, lower is better;
- <strong>CLIP Score</strong>: measure contextual alignment with prompt descriptions, higher is better.
<strong>\[DM Unlearning Tasks\]</strong>:
- NSFW: Nudity
- Style: Van Gogh
- Objects: Church, Tench, Parachute, Garbage Truck
"""
# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
For more details of Unlearning Methods used in this benchmarks:
- [Adversarial Unlearning (AdvUnlearn)](https://github.com/OPTML-Group/AdvUnlearn);
- [Erased Stable Diffusion (ESD)](https://github.com/rohitgandikota/erasing);
- [Forget-Me-Not (FMN)](https://github.com/SHI-Labs/Forget-Me-Not);
- [Ablating Concepts (AC)](https://github.com/nupurkmr9/concept-ablation);
- [Unified Concept Editing (UCE)](https://github.com/rohitgandikota/unified-concept-editing);
- [concept-SemiPermeable Membrane (SPM)](https://github.com/Con6924/SPM);
- [Saliency Unlearning (SalUn)](https://github.com/OPTML-Group/Unlearn-Saliency);
- [EraseDiff (ED)](https://github.com/JingWu321/EraseDiff);
- [ScissorHands (SH)](https://github.com/JingWu321/Scissorhands).
<strong>We will evaluate your model on UnlearnDiffAtk Benchmark!</strong> \\
Open a [github issue](https://github.com/OPTML-Group/Diffusion-MU-Attack/issues) or email us at [email protected]!
"""
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}
}
""" |