The best model to describe the asymmetrical distribution of noise correlation was explored to improve the accuracy of identifying digital image authenticity by noise correlation.By theoretical analysis and experiments,it was indicated that the beta distribution, gamma distribution and the logarithmic normal distribution could be used to describe the asymmetrical distribution of noise correlation within (0,1］ . Then, three kinds of probability density functions were used to simulate the probability density function curves of the actual asymmetrical distribution of noise correlation. On the basis of the curve modality of probability density function and the value of the least false rate,it was explained that using logarithmic normal distribution to describe the asymmetrical distribution of noise correlation could obtain the best result. Thus a model by using the logarithmic normal distribution to describe the asymmetrical distribution of noise correlation was proposed. Experiment results show that this model can reduce the least false rate over 60% as compare with the model of using generalized chi square distribution, which proves that using a correct model to describe the asymmetrical distribution of noise correlation is an effective approach to reduce the false rate.