基于迁移学习的图像隐写分析新方法研究

项目名称： 基于迁移学习的图像隐写分析新方法研究

项目编号： No.U1536120

项目类型： 联合基金项目

立项/批准年度： 2016

项目学科： 管理科学

项目作者： 董晶

作者单位： 中国科学院自动化研究所

项目金额： 63万元

中文摘要： 本课题拟研究基于迁移学习的图像隐写分析新方法。现有的隐写分析方法大都是基于特征分析与传统机器学习的方法的。这类方法在特定的训练样本库上可以获得较好的检测性能。然而，由于隐写载源失配问题，当这些传统的隐写检测器面向当前互联网海量、多源、异质的图像数据进行通用隐写分析时，尤其是对未知图像来源和未知隐写术进行分析时，其检测性能急剧下降。这也是当前隐写分析方法面临的普遍挑战。其原因就在于当测试样本的分布发生变化时，大部分的传统机器学习方法不具备自适应能力，需要对新的分布进行重新学习，并要求提供更多的训练数据。为解决这个问题，我们尝试设计基于迁移学习的隐写分析新方法。迁移学习是一种较新的机器学习方法。它通过利用相关领域的数据和经验，并将相关领域的有用知识“迁移”到目标领域中，能够提高知识的泛化能力。本课题旨在研究对已有隐写方法的迁移，用来提高其对未知数据源及隐写术的检测鲁棒性和泛化性。

中文关键词： 信息隐藏；隐写分析；机器学习；迁移学习

英文摘要： This project is focusing on image steganalysis based on transfer learning. Current steganlaysis technicques are mainly based on heuristic feature extraction and surpervised learning. These methods can achive good detection results on specific but limited testing samples. Due to the cover source mismatch problem of steganalysis, tranditional learning based staganalyzer are usually fail when dealing with the big data scenario, especially when facing the unknown samples without label information. This is also due to that tranditional surpervised learning based steganlysis methods don't have the adaptivity for unknown distribution, unwell trained classifier usually fails on new samples and can not updated online. Our research is focus on steganalysis using transfer learning. Transfer learning is a new machine learning method that applies the knowledge from related but different domains to target domains. It relaxes the two basic assumptions in traditional machine learning: (1)the training (also referred as source domain) and test data (also referred target domain) follow the independent and identically distributed (i.i.d.) condition; (2) there are enough labeled samples to learn a good classification model, aiming to solve the problems that there are few or even not any labeled data in target domains. We also will design the transfer learning algorithms and proper scheme for metric learning as well as local learning. It is significant in both the theoretical and applicative perspectives for the develpment of image steganalysis.

英文关键词： data hiding;steganalysis;machine learning;transfer learning