The intrinsic similarity between camouflaged objects and background environment impedes the automatic detection/segmentation of camouflaged objects, and novel network architectures for deep learning are promising to overcome this challenge and improve detection accuracy. However, these existing network architectures for distinguishing between camouflaged objects and their backgrounds do not account for the constraint of detection speed, which results in high computational complexity and the inability to meet the requirements of rapid detection. Therefore, based on the human visual system, this study proposes a single-stage lightweight camouflage object detection network using multilevel feature fusion, integrating features of various feature layers and receptive field sizes. Using three benchmark datasets for normal camouflaged objects, the lightweight network (LINet) model demonstrated an accuracy superior to those of six existing mainstream camouflaged object detection methods. Its detection speed, 126.3 frames per second, is significantly higher than those of the existing mainstream methods, enabling rapid detection with a maximum increase of 187.62%. The accuracy of LINet is the minimum and maximum for Resnet101 and Resnet152, respectively. These findings pave the way for diverse applications of camouflaged target detection algorithms.

The camouflaged object segmentation model (COSM) has recently gained substantial attention due to its remarkable ability to detect camouflaged objects. Nevertheless, deep vision models are widely acknowledged to be susceptible to adversarial examples, which can mislead models, causing them to make incorrect predictions through imperceptible perturbations. The vulnerability to adversarial attacks raises significant concerns when deploying COSM in security-sensitive applications. Consequently, it is crucial to determine whether the foundational vision model COSM is also susceptible to such attacks. To our knowledge, our work represents the first exploration of strategies for targeting COSM with adversarial examples in the digital world. With the primary objective of reversing the predictions for both masked objects and backgrounds, we explore the adversarial robustness of COSM in full white-box and black-box settings. In addition to the primary objective of reversing the predictions for masked objects and backgrounds, our investigation reveals the potential to generate any desired mask through adversarial attacks. The experimental results indicate that COSM demonstrates weak robustness, rendering it vulnerable to adversarial example attacks. In the realm of COS, the projected gradient descent (PGD) attack method exhibits superior attack capabilities compared to the fast gradient sign (FGSM) method in both white-box and black-box settings. These findings reduce the security risks in the application of COSM and pave the way for multiple applications of COSM.