Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rul...Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rules,resulting in low embedding capacity,while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency.To address this,we propose a sentence attribute encodingbased MLS method that enhances extraction efficiency while maintaining strong performance.The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information.When the attribute values of the cover sentence differ from the secret information to be embedded,a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute,thereby improving the problem of insufficient manual rules.During the extraction,secret information can be extracted solely by employing the semantic attribute analyzer,thereby eliminating the dependence on the paraphrasing generation model.Experimental results show that thismethod achieves an extraction speed of 1141.54 bits/sec,compared with the existing methods,it has remarkable advantages regarding extraction speed.Meanwhile,the stego text generated by thismethod respectively reaches 68.53,39.88,and 80.77 on BLEU,△PPL,and BERTScore.Compared with the existing methods,the text quality is effectively improved.展开更多
In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and...In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and non-linguistic input,and generates natural language text.This survey aims to provide an up-to-date synthesis of core tasks in neural text generation and the architectures adopted to handle these tasks,and draw attention to the challenges in neural text generation.We first outline the mainstream neural text generation frameworks,and then introduce datasets,advanced models and challenges of four core text generation tasks in detail,including AMR-to-text generation,data-to-text generation,and two text-to-text generation tasks(i.e.,text summarization and paraphrase generation).Finally,we present future research directions for neural text generation.This survey can be used as a guide and reference for researchers and practitioners in this area.展开更多
基金supported by the National Natural Science Foundation of China under Grant 61972057Hunan Provincial Natural Science Foundation of China under Grant 2022JJ30623.
文摘Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rules,resulting in low embedding capacity,while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency.To address this,we propose a sentence attribute encodingbased MLS method that enhances extraction efficiency while maintaining strong performance.The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information.When the attribute values of the cover sentence differ from the secret information to be embedded,a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute,thereby improving the problem of insufficient manual rules.During the extraction,secret information can be extracted solely by employing the semantic attribute analyzer,thereby eliminating the dependence on the paraphrasing generation model.Experimental results show that thismethod achieves an extraction speed of 1141.54 bits/sec,compared with the existing methods,it has remarkable advantages regarding extraction speed.Meanwhile,the stego text generated by thismethod respectively reaches 68.53,39.88,and 80.77 on BLEU,△PPL,and BERTScore.Compared with the existing methods,the text quality is effectively improved.
基金the National Natural Science Foundation of China(Grant No.61772036)the Key Laboratory of Science,Technology and Standard in Press Industry(Key Laboratory of Intelligent Press Media Technology)。
文摘In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and non-linguistic input,and generates natural language text.This survey aims to provide an up-to-date synthesis of core tasks in neural text generation and the architectures adopted to handle these tasks,and draw attention to the challenges in neural text generation.We first outline the mainstream neural text generation frameworks,and then introduce datasets,advanced models and challenges of four core text generation tasks in detail,including AMR-to-text generation,data-to-text generation,and two text-to-text generation tasks(i.e.,text summarization and paraphrase generation).Finally,we present future research directions for neural text generation.This survey can be used as a guide and reference for researchers and practitioners in this area.
