Tabares-Soto Reinel, Arteaga-Arteaga Harold Brayan, Mora-Rubio Alejandro, Bravo-Ortíz Mario Alejandro, Arias-Garzón Daniel, Alzate-Grisales Jesús Alejandro, Orozco-Arias Simon, Isaza Gustavo, Ramos-Pollán Raúl
Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia.
Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia.
PeerJ Comput Sci. 2021 Aug 31;7:e616. doi: 10.7717/peerj-cs.616. eCollection 2021.
In recent years, the traditional approach to spatial image steganalysis has shifted to deep learning (DL) techniques, which have improved the detection accuracy while combining feature extraction and classification in a single model, usually a convolutional neural network (CNN). The main contribution from researchers in this area is new architectures that further improve detection accuracy. Nevertheless, the preprocessing and partition of the database influence the overall performance of the CNN. This paper presents the results achieved by novel steganalysis networks (Xu-Net, Ye-Net, Yedroudj-Net, SR-Net, Zhu-Net, and GBRAS-Net) using different combinations of image and filter normalization ranges, various database splits, different activation functions for the preprocessing stage, as well as an analysis on the activation maps and how to report accuracy. These results demonstrate how sensible steganalysis systems are to changes in any stage of the process, and how important it is for researchers in this field to register and report their work thoroughly. We also propose a set of recommendations for the design of experiments in steganalysis with DL.
近年来，空间图像隐写分析的传统方法已转向深度学习（DL）技术，该技术在将特征提取和分类结合在单个模型（通常是卷积神经网络（CNN））中的同时提高了检测精度。该领域研究人员的主要贡献是进一步提高检测精度的新架构。然而，数据库的预处理和划分会影响CNN的整体性能。本文展示了新颖的隐写分析网络（Xu-Net、Ye-Net、Yedroudj-Net、SR-Net、Zhu-Net和GBRAS-Net）使用不同的图像和滤波器归一化范围组合、各种数据库划分、预处理阶段的不同激活函数所取得的结果，以及对激活图的分析和如何报告精度。这些结果表明隐写分析系统对该过程任何阶段的变化是多么敏感，以及该领域的研究人员全面记录和报告他们的工作是多么重要。我们还为使用DL的隐写分析实验设计提出了一组建议。
