  In this work, we leverage visual prompting (VP) to improve adversarial
robustness of a fixed, pre-trained model at testing time. Compared to
conventional adversarial defenses, VP allows us to design universal (i.e.,
data-agnostic) input prompting templates, which have plug-and-play capabilities
at testing time to achieve desired model performance without introducing much
computation overhead. Although VP has been successfully applied to improving
model generalization, it remains elusive whether and how it can be used to
defend against adversarial attacks. We investigate this problem and show that
the vanilla VP approach is not effective in adversarial defense since a
universal input prompt lacks the capacity for robust learning against
sample-specific adversarial perturbations. To circumvent it, we propose a new
VP method, termed Class-wise Adversarial Visual Prompting (C-AVP), to generate
class-wise visual prompts so as to not only leverage the strengths of ensemble
prompts but also optimize their interrelations to improve model robustness. Our
experiments show that C-AVP outperforms the conventional VP method, with 2.1X
standard accuracy gain and 2X robust accuracy gain. Compared to classical
test-time defenses, C-AVP also yields a 42X inference time speedup.
