Camouflage, a vital survival strategy in nature, allows organisms to evade predators through environmental mimicry. However, the high similarity between camouflaged objects and backgrounds in color, texture, and contour poses significant detection challenges. While recent studies have achieved promising progress, they mainly focus on spatial features and lack multi-scale and cross-domain fusion. To this end, we propose WaveCamoNet, a cross-domain fusion model for camouflaged object detection. The model extracts frequency domain features by wavelet transform and fuses them with multi-scale spatial features to enhance semantic representation. Also, we design a texture enhancement module to refine high-frequency details and suppress background noise. Experiments on three challenging benchmark datasets demonstrate that our WaveCamoNet significantly outperforms the existing state-of-the-art CNN-based methods under four widely-used evaluation metrics.