A novel method for linguistic steganography by English translation using attention mechanism and probability distribution theory.

ABSTRACT: To enhance our ability to model long-range semantical dependencies, we introduce a novel approach for linguistic steganography through English translation. This method leverages attention mechanisms and probability distribution theory, known as NMT-stega (Neural Machine Translation-steganography). Specifically, to optimize translation accuracy and make full use of valuable source text information, we employ an attention-based NMT model as our translation technique. To address potential issues related to the degradation of text quality due to secret information embedding, we have devised a dynamic word pick policy based on probability variance. This policy adaptively constructs an alternative set and dynamically adjusts embedding capacity at each time step, guided by variance thresholds. Ad

SUBMITTER: Lin Y 

PROVIDER: S-EPMC10760771 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature