  Deep neural networks (DNNs) can be manipulated to exhibit specific behaviors
when exposed to specific trigger patterns, without affecting their performance
on benign samples, dubbed \textit{backdoor attack}. Currently, implementing
backdoor attacks in physical scenarios still faces significant challenges.
Physical attacks are labor-intensive and time-consuming, and the triggers are
selected in a manual and heuristic way. Moreover, expanding digital attacks to
physical scenarios faces many challenges due to their sensitivity to visual
distortions and the absence of counterparts in the real world. To address these
challenges, we define a novel trigger called the \textbf{V}isible,
\textbf{S}emantic, \textbf{S}ample-Specific, and \textbf{C}ompatible (VSSC)
trigger, to achieve effective, stealthy and robust simultaneously, which can
also be effectively deployed in the physical scenario using corresponding
objects. To implement the VSSC trigger, we propose an automated pipeline
comprising three modules: a trigger selection module that systematically
identifies suitable triggers leveraging large language models, a trigger
insertion module that employs generative models to seamlessly integrate
triggers into images, and a quality assessment module that ensures the natural
and successful insertion of triggers through vision-language models. Extensive
experimental results and analysis validate the effectiveness, stealthiness, and
robustness of the VSSC trigger. It can not only maintain robustness under
visual distortions but also demonstrates strong practicality in the physical
scenario. We hope that the proposed VSSC trigger and implementation approach
could inspire future studies on designing more practical triggers in backdoor
attacks.
