A novel hybrid network model for image steganalysis

Steganalysis attempts to discover hidden signals in suspected carriers or at the least detect which media contain hidden signals. Conventional approaches to steganalysis depend on artificially designed image features. However, these methods are time-consuming and labor-intensive. Additionally, the statistical methods may not produce optimal outcomes. Deep learning-based steganalysis algorithms which use convolutional neural network (CNN) structures, such as ZhuNet, obviate the need for artificially design features while optimizing the feature extraction and classification processes via training and learning. This approach greatly boosts the applicability and effectiveness of steganalysis. Nevertheless, it is important to note that CNN-based steganalysis algorithms do have some limitations. To begin with, the feature extraction of stego images, which relies on deep neural networks, lacks consideration for the interdependence of local features when constructing the overall feature map. Furthermore, CNN-based steganalysis models use all features indiscriminately to classify stego images, which can potentially reduce the models' accuracy. Based on ZhuNet, we provide a novel hybrid network model known as ZhuNet-ATT-BiLSTM in order to tackle the aforementioned concerns. This model introduces a Bidirectional Long Short-Term Memory (BiLSTM) structure to mutually learn about the relationships between image features to ensure comprehensive utilization of stego image features. In addition, an attention mechanism is integrated for steganalysis to dynamically allocate weights to feature data, amplifying the signal for the vital features while effectively attenuating the less important and irrelevant features. Lastly, the enhanced model is verified with two open datasets: Bossbase 1.01 and COCO. According to experimental findings, the proposed hybrid network model improves the image steganalysis accuracy by comparing with earlier methods.