Universal Prompt Optimizer for Safe Text-to-Image Generation
Zongyu Wu Hongcheng Gao Yueze Wang Xiang Zhang Suhang Wang
Text-to-Image (T2I) models have shown great performance in generating images
based on textual prompts. However, these models are vulnerable to unsafe input
to generate unsafe content like sexual, harassment and illegal-activity images.
Existing studies based on image checker, model fine-tuning and embedding
blocking are impractical in real-world applications. Hence, \textit{we propose
the first universal prompt optimizer for safe T2I generation in black-box
scenario}. We first construct a dataset consisting of toxic-clean prompt pairs
by GPT-3.5 Turbo. To guide the optimizer to have the ability of converting
toxic prompt to clean prompt while preserving semantic information, we design a
novel reward function measuring toxicity and text alignment of generated images
and train the optimizer through Proximal Policy Optimization. Experiments show
that our approach can effectively reduce the likelihood of various T2I models
in generating inappropriate images, with no significant impact on text
alignment. It is also flexible to be combined with methods to achieve better
performance.