The method of describing deformation camouflage spots based on feature space has some shortcomings, such as inaccurate description and difficult reproduction. Depending on the strong fitting ability of the generative adversarial network model, the distribution of deformation camouflage spot patt can be directly fitted, thus simplifying the process of spot extraction and reproduction. The requirements of background spot extraction are analyzed theoretically. The calculation formula of limiting the range of image spot pixels is given and two kinds of spot data sets, forestland and snowfield, are established. Spot feature is decomposed into shape, size and color features, and a GAN (Generative Adversarial Network) framework is established. The effects of different loss functions on network training results are analyzed in the experiment. In the meantime, when the input dimension of generator network is 128, the balance between sample diversity and quality can be achieved. The effects of sample generation are investigated in two aspects. Subjectively, the probability of the generated spots being distinguished in the background is counted, and the results are all less than 20％and mostly close to zero. Objectively, the features of the spot shape are calculated and the independent sample T-test is applied to verify that the features are from the same distribution, and all the P-Values are much higher than 0.05. Both subjective and objective methods prove that the spots generated by this method are similar to the background spots. The pro-posed method can directly generate the desired camouflage patt spots, which provides a new tech-nical method for the deformation camouflage patt design and camouflage effect evaluation.