Preprocessing Enhancement Method for Spatial Domain Steganalysis
Xueming Duan, Chunying Zhang, Yingshuo Ma, Shouyue Liu · Mathematics · 2022
In the field of steganalysis, in recent years, the research focus has mostly been on optimizing the structures of neural networks, while the application of high-pass filters is still limited to the simple selection of filters and simple adjustment of the number of filters. In this paper, we propose a method to enhance the assistance and contribution of high-pass filters to the detection capability of a spatial domain steganalysis model, which mainly contains the preprocessing enhancement of high-pass filters and cross-layer enhancement of high-pass filters, and we construct a preprocessing enhancement model, the HPF-Enhanced Model, for spatial domain steganalysis, based on Yedroudj-Net. In the experimental part, we find the best preprocessing enhancement method through various validations, and we compare the HPF-Enhanced Model with the classical models. The results show that the proposed enhancement method can bring a significant improvement, and they also show that the preprocessing enhancement method can help to reduce the model size, and it thus can be used to construct a lightweight spatial domain steganalysis model with strong performance.