基于特征关联与校准机制的图像隐写分析研究

隐写分析是信息安全领域中新发展起来的一个重要研究内容，其主要目的是揭示数字载体中是否存在隐秘信息，是对抗非法信息传播的重要手段，也是衡量信息隐藏系统安全性的一个重要标准。本课题拟对图像隐写分析中涉及的关联性敏感特征提取、特征校准、隐写分析优化策略等关键内容进行深入研究，力图降低图像隐写分析的计算复杂度、提高检测精度、增强检测的通用性与实用性。课题的主要研究内容包括：基于多重关联特性的抵抗高不可检测隐写(HUGO)的图像隐写分析方法、混合ROF模型与LLT模型的校准图像构造方法、基于图像内容一致性模型的全盲隐写分析方法、特征选择与分类器参数调优、隐写分析原型系统建立等。通过课题的研究，将在抵御HUGO的隐写分析与全盲隐写分析等方面完善图像隐写分析的相关理论和方法，将为提高图像隐写分析的检测精度和通用性提供新方法，为推动图像隐写分析的发展提供技术支撑。

Steganalysis is a new important research hot topic in the field of information security. Its main purpose is to reveal the presence of secret information in digital carrier. It is not only a mean of combating the illegal information transmission, but also an important measure to measure the security of the information hiding system. Despite of substantial advances, image steganalysis is still challenging due to its high computational complexity, low precision and poor universality and practicability. Therefore,this project is motivated to further research the associated sensitive feature extraction, feature calibration, optimization strategy, etc. for degining excellent image steganalysis methods. The main research contents include: the image steganalysis based on multiple associated properties for highly undetectable steganography, the construction of calibrated image based on ROF model and LLT model, the full-blind steganalysis method based on image content consistency model, the feature selection and parameter optimization of the classifier, and the implementation of steganalysis prototype system, etc. Through the research, the related theory and method will be consummated in steganalysis for detecting HUGO and full-blind steganalysis. And the new methods will be proposed to improve the detection precision and universality for image steganalysis. This project also provides the technical support to promote the development of image steganalysis.