  SAM is a segmentation model recently released by Meta AI Research and has
been gaining attention quickly due to its impressive performance in generic
object segmentation. However, its ability to generalize to specific scenes such
as camouflaged scenes is still unknown. Camouflaged object detection (COD)
involves identifying objects that are seamlessly integrated into their
surroundings and has numerous practical applications in fields such as
medicine, art, and agriculture. In this study, we try to ask if SAM can address
the COD task and evaluate the performance of SAM on the COD benchmark by
employing maximum segmentation evaluation and camouflage location evaluation.
We also compare SAM's performance with 22 state-of-the-art COD methods. Our
results indicate that while SAM shows promise in generic object segmentation,
its performance on the COD task is limited. This presents an opportunity for
further research to explore how to build a stronger SAM that may address the
COD task. The results of this paper are provided in
\url{https://github.com/luckybird1994/SAMCOD}.
