Image steganalysis is to discriminate innocent images (cover images) and those suspected images (stego images) with hidden messages. The task is challenging since modifications to cover images due to message hiding are extremely small. To handle this difficulty, modern approaches proposed using convolutional neural network (CNN) models to detect steganography with paired learning, i.e., cover images and their stegos are both in training set. In this paper, we explore an important technique in CNN models, the batch normalization (BN), for the task of image steganalysis in the paired learning framework. Our theoretical analysis shows that a CNN model with multiple batch normalization layers is difficult to be generalized to new data in the test set when it is well trained with paired learning. To address this problem, we propose a novel normalization technique called shared normalization (SN) in this paper. Unlike the BN layer utilizing the mini-batch mean and standard deviation to normalize each input batch, SN shares consistent statistics for training samples. Based on the proposed SN layer, we further propose a novel neural network model for image steganalysis. Extensive experiments demonstrate that the proposed network with SN layers is stable and can detect the state-of-the-art steganography with better performances than previous methods.