Recent steganalytic schemes reveal embedding traces in a promising way by using Convolutional Neural Networks (CNNs). However, further improvements, such as exploring complementary data processing operations and using wider structures, were not extensively studied so far. In this paper, we design a new CNN in these aspects in order to better capture embedding artifacts. Specifically, on the one hand, we propose to process information diversely with a module called diverse activation module. On the other hand, we build a wide structure with parallel sub-nets using several filter groups for pre-processing. To accelerate the training process, we pre-train the sub-nets independently and feed their output vectors together to a classification module. Extensive experiments show that the proposed method is effective in detecting content-adaptive steganographic schemes.