Digital watermarking method based on context word prediction and window compression coding - Lingyun XIANG; Minghao HUANG; Chenling ZHANG; Chunfang YANG (Tongxin xuebao)
To address the problems of limited number of substitutable words and low watermark extraction efficiency in the existing natural language digital watermarking methods, a creative method based on context word prediction and window compression coding was proposed.Firstly, the contextual semantic features of each word in the original text were automatically learned through a neural network language model, and then the candidate word set for each word was predicted, thus the number of substitutable words that could be utilized for carrying watermark information was expanded.Meanwhile, considering the difference of the semantic impact caused by the substitutions of candidate words at different positions, the watermark information was embedded into each window containing several words, and the selection of candidate words for watermark embedding was optimized by the similarity between sentences before and after performing word substitutions.Finally, a semantic-independent window compression coding method was proposed, which encoded each window as appointed watermark information in terms of the character information of words contained in the window.So that during watermark extraction, the dependence on the original context at the position of word substitution was eliminated.The experimental results show that the proposed method greatly improves the watermark extraction efficiency with high embedding capacity and text quality.