LGS-Net: A lightweight convolutional neural network based on global feature capture for spatial image steganalysis

The purpose of image steganalysis is to detect whether the transmitted images in network communication contain secret messages. Current image steganalysis networks still have some problems such as inappropriate feature selection and easy overfitting. Therefore, this paper proposed a new spatial image steganalysis method based on convolutional neural networks. To extract richer features while reducing useless parameters in the network, this paper introduced the Im SRM filtering kernel into the image preprocessing module. To extract effective steganography noise from images, this paper combined depthwise separable convolution and residual networks for the first time and introduces them into the steganography noise extraction module. In addition, to focus network attention on the image regions where steganography information exists, this paper integrated the coordinate attention mechanism. This module will make the network pay attention to the overall structure and local details of the image during network training, improving the network's recognition ability for steganography information. Finally, the extracted steganography features are classified through a classification module. This paper conducted a series of experiments on the BOSSBase 1.01 and BOWS2 datasets. The improvement in detection accuracy is between 1.2% and 18.2% compared to classic and recent steganalysis networks.