基于深度学习的图像隐写分析综述

隐写术及隐写分析是信息安全领域研究热点之一.隐写术的滥用造成许多安全隐患，如：非法分子利用隐写进行隐蔽通信完成恐怖袭击.传统隐写分析方法的设计需要大量先验知识，而基于深度学习的隐写分析方法利用网络强大的表征学习能力自主提取图像异常特征，大大减少了人为参与，取得了较好的研究效果.为了促进基于深度学习的隐写分析方法研究，本文对目前隐写分析领域的主要方法和突破性工作进行了分析与总结.首先，本文比较了传统隐写分析方法与基于深度学习的隐写分析方法的差异；然后，根据训练方式不同将基于深度学习的隐写分析模型分为两类：半学习隐写分析模型与全学习隐写分析模型，详细介绍了基于深度学习的各类隐写分析网络结构与检测效果；其次，分析总结了对抗样本对深度学习安全带来的挑战，并阐述了基于隐写分析的对抗样本检测方法；最后，本文总结了现有基于深度学习的隐写分析模型存在的优缺点，并探讨了基于深度学习的隐写分析模型的发展趋势.

Review of Image Steganalysis Based on Deep Learning

Steganography and steganalysis are one of the research hotspots in the field of information security. The abuse of steganography has caused many potential safety hazard. For example, illegal elements use steganography for covert communications to carry out terrorist attacks. The design of traditional steganalysis methods requires a large amount of prior knowledge, and the steganalysis methods based on deep learning use the powerful representation learning ability of the network to autonomously extract abnormal image features, which greatly reduces human participation and achieves good results. To promote the research of steganalysis technology based on deep learning, this paper analyzes and summarizes the main methods and work in the field of steganalysis. Firstly, this paper analyzes and compares the differences between traditional steganalysis and deep learning-based steganalysis. Furthermore, according to the different training methods, the steganalysis models based on deep learning are divided into two categories:Semi-learning steganalysis model and Full-learning steganalysis model. The network structure and detection effect of various types of steganalysis based on deep learning are introduced in detail. In addition, we analyzed and summarized the challenges that the adversarial samples pose to deep learning security, expounds the detection method of adversarial samples based on steganalysis. Finally, this paper summarizes the pros and cons of existing steganalysis models based on deep learning and discusses its development trends.