Least significant bit (LSB) matching algorithm and common steganographic methods, which use Gaussian support vector machine (GSVM) algorithm as the classifier, spend too much training time. Therefore, an improved logistic regression classifying algorithm named L curve truncated regularized iteratively re-weighted least squares(LTR IRLS) is proposed. Firstly, near optimal parameters of Tikhonov regularization are determined based on L curve, and convergence parameters of the truncated Newton algorithm are obtained through experiments for increasing the detection accuracy. Secondly, iteratively re-weighted least squares are utilized to search for the maximum loss expectancy and truncated Newton methods are utilized to avoid computing the Hessian matrix in the objective function, therefore reducing the computation amount greatly. Theoretical analysis and experimental results verify that LTR IRLS can ensure the detection accuracy rate higher than GSVM classifier, meanwhile reducing the training time and increaseing the detection speed.