A comprehensive evaluation of deep learning based steganalysis performance in detecting spatial methods

Steganalysis is the art of detecting the existence of hidden data. Recent research has revealed that convolutional neural networks (CNNs) can detect data through automatic feature extraction. Several studies investigated the performance of existing models using a limited number of spatial steganography methods. This study aims to propose a CNN and comprehensively investigate its efficiency in detecting different spatial methods. The proposed model comprises three modules: preprocessing, convolutional (five blocks), and classifier (three fully connected layers). The test results for the least-significant-bit (LSB) and pixel-value differencing (PVD) based methods indicate that the proposed method can detect data of even concise length with high accuracy and a low error. The proposed method also detects complexity-based LSB-M (CBL) as an adaptive approach. Lower embedding rates make this success even more impressive. Manual feature extraction has much lower success rates due to low variations of statistical features at low embedding rates than the proposed model.