This paper deals with the Cover-Source Mismatch (CSM) problem faced in operational steganalysis. Based on a multivariate Gaussian model of the distribution of the noise contained in natural images, it provides proxies for the two important empirical measures of CSM: intrinsic difficulty and regret. The former can be modeled with the determinant of the covariance matrix of the noise present in an image. The latter can be predicted with a modified Kullback-Leibler divergence between the distribution of the noises of images coming from different cover-sources. We first recall the reasoning behind the multivariate Gaussian model of the noise, and detail how to compute the statistic of the distribution of the noise. Then, our proposed models are compared to empirical data with a specifically designed cover-source generation process. For both quantities, very high correlation coefficients between the model and the observations are obtained. Finally, realistic cover-sources are used to further illustrate the relevance of our model.