Vision Transformers have recently demonstrated strong capabilities in modeling the global context for camouflaged object detection. However, existing transformer-based approaches still struggle in complex scenarios involving small, multiple, and occluded objects. Their manually designed attention mechanisms often fail to adapt to data-specific characteristics, thereby limiting the model’s ability to capture long-range dependencies. Moreover, many of these methods are insufficient in capturing fine-grained local details. To address these limitations, we present a novel framework named SHNet. Specifically, we propose a spectral bias injection module (SBIM), which injects spectral deviation signals into the standard convolutional pathway to enhance the detection of small camouflaged objects. Then, we design a frequency attention module (FAM) based on the HiLo attention mechanism. By jointly modeling hierarchical dependencies across global and local contexts it improves performance in scenes with multiple and occluded objects. Furthermore, we design a plug-and-play interactive fusion module (IFM) that adaptively performs fine-grained feature selection and aggregates complementary information across different levels. Extensive experiments on four widely used datasets demonstrate the effectiveness and efficiency of the proposed method.