A Customized Convolutional Neural Network with Low Model Complexity for JPEG Steganalysis

Nowadays, convolutional neural network (CNN) is appied to different types of image classification tasks and outperforms almost all traditional methods. However, one may find it difficult to apply CNN to JPEG steganalysis because of the extremely low SNR (embedding messages to image contents) in the task. In this paper, a selection-channel-aware CNN for JPEG steganalysis is proposed by incorporating domain knowledge. Specifically, instead of random strategy, kernels of the first convolutional layer are initialized with hand-crafted filters to suppress the image content. Then, truncated linear unit (TLU), a heuristically-designed activation function, is adopted in the first layer as the activation function to better adapt to the distribution of feature maps. Finally, we use a generalized residual learning block to incorporate the knowledge of selection channel in the proposed CNN to further boost its performance. J-UNIWARD, a state-of-the-art JPEG steganographic scheme, is used to evaluate the performance of the proposed CNN and other competing JPEG steganalysis methods. Experiment results show that the proposed CNN steganalyzer outperforms other feature-based methods and rivals the state-of-the-art CNN-based methods with much reduced model complexity, at different payloads.