Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image
  Models
Yuzhu Cai Sheng Yin Yuxi Wei Chenxin Xu Weibo Mao Felix Juefei-Xu Siheng Chen Yanfeng Wang
The burgeoning landscape of text-to-image models, exemplified by innovations
such as Midjourney and DALLE 3, has revolutionized content creation across
diverse sectors. However, these advancements bring forth critical ethical
concerns, particularly with the misuse of open-source models to generate
content that violates societal norms. Addressing this, we introduce
Ethical-Lens, a framework designed to facilitate the value-aligned usage of
text-to-image tools without necessitating internal model revision. Ethical-Lens
ensures value alignment in text-to-image models across toxicity and bias
dimensions by refining user commands and rectifying model outputs. Systematic
evaluation metrics, combining GPT4-V, HEIM, and FairFace scores, assess
alignment capability. Our experiments reveal that Ethical-Lens enhances
alignment capabilities to levels comparable with or superior to commercial
models like DALLE 3, ensuring user-generated content adheres to ethical
standards while maintaining image quality. This study indicates the potential
of Ethical-Lens to ensure the sustainable development of open-source
text-to-image tools and their beneficial integration into society. Our code is
available at https://github.com/yuzhu-cai/Ethical-Lens.