Towards Implicit Prompt For Text-To-Image Models
arxiv(2024)
摘要
Recent text-to-image (T2I) models have had great success, and many benchmarks
have been proposed to evaluate their performance and safety. However, they only
consider explicit prompts while neglecting implicit prompts (hint at a target
without explicitly mentioning it). These prompts may get rid of safety
constraints and pose potential threats to the applications of these models.
This position paper highlights the current state of T2I models toward implicit
prompts. We present a benchmark named ImplicitBench and conduct an
investigation on the performance and impacts of implicit prompts with popular
T2I models. Specifically, we design and collect more than 2,000 implicit
prompts of three aspects: General Symbols, Celebrity Privacy, and
Not-Safe-For-Work (NSFW) Issues, and evaluate six well-known T2I models'
capabilities under these implicit prompts. Experiment results show that (1) T2I
models are able to accurately create various target symbols indicated by
implicit prompts; (2) Implicit prompts bring potential risks of privacy leakage
for T2I models. (3) Constraints of NSFW in most of the evaluated T2I models can
be bypassed with implicit prompts. We call for increased attention to the
potential and risks of implicit prompts in the T2I community and further
investigation into the capabilities and impacts of implicit prompts, advocating
for a balanced approach that harnesses their benefits while mitigating their
risks.
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