This paper proposes a novel approach to improve camouflaged object detection (COD) techniques using a data augmentation strategy based on a diffusion model. COD represents a significant challenge in computer vision, being crucial in applications such as military surveillance, medical image analysis, and ecological studies. Although recent advances in deep learning have improved COD performance, they heavily rely on well-annotated datasets, which are particularly difficult to obtain in the context of camouflaged objects. The proposed method addresses the problem of data sparsity while maintaining crucial features of camouflaged objects and their relationships with the environment. Incorporating a diffusion model into the data augmentation pipeline shows improvement in the performance of the COD models, which varies between the different techniques and the proposed evaluation metrics.