  Co-salient object detection (CoSOD) aims to identify the common and salient
(usually in the foreground) regions across a given group of images. Although
achieving significant progress, state-of-the-art CoSODs could be easily
affected by some adversarial perturbations, leading to substantial accuracy
reduction. The adversarial perturbations can mislead CoSODs but do not change
the high-level semantic information (e.g., concept) of the co-salient objects.
In this paper, we propose a novel robustness enhancement framework by first
learning the concept of the co-salient objects based on the input group images
and then leveraging this concept to purify adversarial perturbations, which are
subsequently fed to CoSODs for robustness enhancement. Specifically, we propose
CosalPure containing two modules, i.e., group-image concept learning and
concept-guided diffusion purification. For the first module, we adopt a
pre-trained text-to-image diffusion model to learn the concept of co-salient
objects within group images where the learned concept is robust to adversarial
examples. For the second module, we map the adversarial image to the latent
space and then perform diffusion generation by embedding the learned concept
into the noise prediction function as an extra condition. Our method can
effectively alleviate the influence of the SOTA adversarial attack containing
different adversarial patterns, including exposure and noise. The extensive
results demonstrate that our method could enhance the robustness of CoSODs
significantly.
