Pdf Segfree Segmentation Free Generative Linguistic Steganographic Approach For Unsegmented

(PDF) SegFree: Segmentation-Free Generative Linguistic Steganographic Approach For Unsegmented ...

(PDF) SegFree: Segmentation-Free Generative Linguistic Steganographic Approach For Unsegmented ... In this paper, we propose segfree, a segmentation free generative linguistic steganographic approach for unsegmented languages. In contrast to this, this paper proposes a segmentation free (segfree) approach, where the mt model receives an unsegmented stream of source text and generates a continuous sequence of translated words.

(PDF) Boosting N-gram Coverage For Unsegmented Languages Using Multiple Text Segmentation ...

(PDF) Boosting N-gram Coverage For Unsegmented Languages Using Multiple Text Segmentation ... In this paper, we propose segfree, a segmentation free generative linguistic steganographic approach for unsegmented languages. first, we present an adaptive checksum verification method to filter errors caused by segmentation ambiguity. This paper proposes a segmentation free (segfree) approach that does not rely on an up stream segmenter. instead, the mt model receives an unsegmented stream of source text and gener ates a continuous sequence of translated words. Segfree: segmentation free generative linguistic steganographic approach for unsegmented languages 2023 10 02 | preprint doi: 10.36227/techrxiv.24199068 contributors: ruiyi yan; tianjun song; yating yang. In this paper, we propose segfree, a segmentation free generative linguistic steganographic approach for unsegmented languages. first, we present an adaptive checksum verification method to filter errors caused by segmentation ambiguity.

Generative Text Steganography With Large Language Model | AI Research Paper Details

Generative Text Steganography With Large Language Model | AI Research Paper Details Segfree: segmentation free generative linguistic steganographic approach for unsegmented languages 2023 10 02 | preprint doi: 10.36227/techrxiv.24199068 contributors: ruiyi yan; tianjun song; yating yang. In this paper, we propose segfree, a segmentation free generative linguistic steganographic approach for unsegmented languages. first, we present an adaptive checksum verification method to filter errors caused by segmentation ambiguity. Since tokenization serves a fundamental preprocessing step in numerous language models, tokens naturally constitute the basic embedding units for generative lin. This paper addresses segmentation ambiguity in gen erative linguistic steganography from the perspective of tokenization consistency, with the goal of minimizing the negative impact of disambiguation. In this paper, we propose segfree, a segmentation free generative linguistic steganographic approach for unsegmented languages. To enhance steganographic security, meanwhile addressing segmentation ambiguity, we propose a secure and disambiguating approach for linguistic steganography. in this letter, we focus on two questions: (1) which candidate pools should be modified? (2) which tokens should be retained?.

A generative model for unsupervised word segmentation