Natural language watermarking for german texts

Abstract

In this paper we present four informed natural language watermark embedding methods, which operate on the lexical and syntactic layer of German texts. Our scheme provides several benefits in comparison to state-of-the-art approaches, as for instance that it is not relying on complex NLP operations like full sentence parsing, word sense disambiguation, named entity recognition or semantic role parsing. Even rich lexical resources (e.g. WordNet or the Collins thesaurus), which play an essential role in many previous approches, are unnecessary for our system. Instead, our methods require only a Part-Of-Speech Tagger, simple wordlists that act as black- and whitelists and a trained classifier, which automatically predicts the ability of potential lexical or syntactic patterns to carry portions of the watermark message. Besides this, a part of the proposed methods can be easily adapted into other Indo-European languages, since the grammar rules the methods rely on are not restricted only to the German language. Because the methods perform only lexical and minor syntactic transformations, the watermarked text is not affected by grammatical distortion and simultaneously the meaning of the text is preserved in 82.14% of the cases.