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Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However the existing camouflaged generation methods require specifying the background manually thus failing to extend the camouflaged sample diversity in a low-cost manner. In this paper we propose a Latent Background Knowledge Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. To our knowledge our contributions mainly include:(1) For the first time we propose a camouflaged generation paradigm that does not need to receive any background inputs.(2) Our LAKE-RED is the first knowledge retrieval-augmented method with interpretability for camouflaged generation in which we propose an idea that knowledge retrieval and reasoning enhancement are separated explicitly to alleviate the task-specific challenges. Moreover our method is not restricted to specific foreground targets or backgrounds offering a potential for extending camouflaged vision perception to more diverse domains.(3) Experimental results demonstrate that our method outperforms the existing approaches generating more realistic camouflage images.