Trigger without Trace: Towards Stealthy Backdoor Attack on Text-to-Image Diffusion Models
Paper β’ 2503.17724 β’ Published
How to use RobinWZQ/backdoor_KMMD_len_20_a_motor with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("RobinWZQ/backdoor_KMMD_len_20_a_motor")
model = AutoModel.from_pretrained("RobinWZQ/backdoor_KMMD_len_20_a_motor")This repository contains artifacts and code related to the paper: [Trigger without Trace: Towards Stealthy Backdoor Attack on Text-to-Image Diffusion Models]
Code: https://github.com/Robin-WZQ/TwT
This study introduces TwT, an attack method based on syntactic structures that exhibits strong resistance to advanced detection methods.
If you find this project useful in your research, please consider cite:
@misc{zhang2025twt,
title={Trigger without Trace: Towards Stealthy Backdoor Attack on Text-to-Image Diffusion Models},
author={Jie Zhang and Zhongqi Wang and Shiguang Shan and Xilin Chen},
year={2025},
eprint={2503.17724},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2503.17724},
}