Enhancing the performance of convolutional neural network image-based steganalysis in spatial domain using Spatial Rich Model and 2D Gabor filters

Image-based steganalysis problem has attracted many researchers, and several solutions have been proposed. Deep learning-based methods are the most promising as they provide superior performance. Convolutional Neural network(CNN) based steganalysis methods are designed to improve the detection rate. Unlike traditional CNN models, CNN-based steganalysis requires careful design of preprocessing layers with filter initialization to obtain a good performance. In this paper, we established a CNN model that consists of two convolution layers for preprocessing and feature extraction, and four fully connected layers for classification. The preprocessing layer uses a set of efficient filter banks consisting of SRM and 2D Gabor filters. We conducted experiments using grayscale cover images from a popular and publicly available BOSSbase_1.01 database and Alask_v2 database with consideration for two different image sizes. The results showed that the proposed CNN model outperforms many state-of-the-art studies in two out of three well-known adaptive spatial domain steganography algorithms (S-UNIWARD, HUGO) and provides a close result for (WOW) algorithm when using the database with 512 × 512 images. On the other hand, the proposed model outperforms many state-of-the-art studies in the three algorithms when using the database with the original image size (256 × 256). Using image size 256, and the S-UNIWARD algorithm, the proposed model improved the detection accuracy rate by 13%, and 4.25% payloads of 0.2 and 0.4 bpp respectively compared to the previously best-known model (GBRAS-Net). The proposed model achieved 7.4% and 6.27% improvement in the detection accuracy for both payloads 0.2 and 0.4 bpp respectively using the HUGO algorithm compared with the previously best-known model (GBRAS-Net). For the WOW algorithm, the proposed model is slightly behind the best model (GBRAS-Net) but was able to obtain a close result for both payloads of 0.2 and 0.4 bpp, respectively. Using an image size of 512, the proposed model achieved 31.26%, 21.51%, 6.84%, 4.22%, and 1.96% improvement in the detection rate for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over S-UNIWARD algorithm compared to the previously best-known model (H-CNN). In addition, the proposed model achieved 27.60%, 23.69%, 12.66%, 5.27%, and 6.23% improved detection accuracy for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over HUGO algorithm compared with the previously best-known model (H-CNN). Finally, the proposed model provided 57.81%, 46.84%, 28.29%, 20.34%, and 13.79% improvement in the detection rate for the five payloads 0.1, 0.2, 0.3, 0.4, and 0.5 bpp respectively over WOW algorithm compared to the previously best-known model (H-CNN).