Steganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which typically requires training a classifier with "laboratory" data. However, applying machine-learning classification to a new source of data is challenging, since there is typically a mismatch between the training and the testing sets. In addition, other sources of uncertainty affect the steganlytic process, including the mismatch between the targeted and the true steganographic algorithms, unknown parameters -- such as the message length -- and even having a mixture of several algorithms and parameters, which would constitute a realistic scenario. This paper presents subsequent embedding as a valuable strategy that can be incorporated into modern steganalysis. Although this solution has been applied in previous works, a theoretical basis for this strategy was missing. Here, we cover this research gap by introducing the "directionality" property of features with respect to data embedding. Once this strategy is sustained by a consistent theoretical framework, new practical applications are also described and tested against standard steganography, moving steganalysis closer to real-world conditions.
翻译:斯特格分析是用来检测秘密信息是否嵌入使用线谱法的载体的一套技术的集合。 多数现有的细化分析方法都是基于机器学习, 通常需要用“ 实验室” 数据来培训分类员。 但是, 将机器学习分类应用于新的数据源是具有挑战性的, 因为通常培训与测试组之间有不匹配。 此外, 其他不确定性来源影响着系统化过程, 包括目标算法与真实的线谱算法、 未知参数 -- -- 例如信息长度 -- -- 甚至有几种算法和参数的混合, 这可以构成一种现实的设想。 本文介绍了随后作为有价值的战略嵌入, 可以纳入现代系统分析中。 尽管在以前的工程中应用了这一解决方案, 但是缺少了这一战略的理论基础。 在这里, 我们通过引入数据嵌入特征的“ 直线性” 属性来覆盖这一研究差距。 一旦这一战略得到一致的理论框架的支持, 新的实际应用条件也会被描述和测试, 以标准的索格分析方法更接近现实世界。