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Data Sci."],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>The core challenge of steganography is always how to improve the hidden capacity and the concealment. Most current generation-based linguistic steganography methods only consider the probability distribution between text characters, and the emotion and topic of the generated steganographic text are uncontrollable. Especially for long texts, generating several sentences related to a topic and displaying overall coherence and discourse-relatedness can ensure better concealment. In this article, we address the problem of generating coherent multi-sentence texts for better concealment, and a topic-aware neural linguistic steganography method that can generate a steganographic paragraph with a specific topic is present. We achieve a topic-controllable steganographic long text generation by encoding the related entities and their relationships from Knowledge Graphs. Experimental results illustrate that the proposed method can guarantee both the quality of the generated steganographic text and its relevance to a specific topic. The proposed model can be widely used in covert communication, privacy protection, and many other areas of information security.<\/jats:p>","DOI":"10.1145\/3418598","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T16:54:59Z","timestamp":1617900899000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Topic-aware Neural Linguistic Steganography Based on Knowledge Graphs"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3974-0690","authenticated-orcid":false,"given":"Yamin","family":"Li","sequence":"first","affiliation":[{"name":"Hubei University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hubei University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongliang","family":"Yang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ru","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Haidian Qu, Beijing Shi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT\u201918)","author":"Ammar Waleed","year":"2018","unstructured":"Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Michael Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler C. 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