In recent years, protecting important objects by simulating animal camouflage has been widely used in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult than traditional object detection techniques because of the high degree of fusion of camouflaged objects with the background. In this paper, we strive to more accurately identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier, called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules, i.e., the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a region. The two sets of output camouflaged object maps are merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, is designed based on two modules to give higher attention to the camouflaged object. Extensive experiments demonstrate that compared with 14 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics on the public COD dataset, and the segmentation accuracy is significantly improved. We also validate the models' generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explore some extended applications of COD.