{"id":"https://openalex.org/W2920628035","doi":"https://doi.org/10.1109/lsp.2019.2902095","title":"A Fast and Efficient Text Steganalysis Method","display_name":"A Fast and Efficient Text Steganalysis Method","publication_year":2019,"publication_date":"2019-02-27","ids":{"openalex":"https://openalex.org/W2920628035","doi":"https://doi.org/10.1109/lsp.2019.2902095","mag":"2920628035"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2019.2902095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2019.2902095","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020864189","display_name":"Zhongliang Yang","orcid":"https://orcid.org/0000-0002-8027-9560"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongliang Yang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8027-9560","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100768896","display_name":"Yongfeng Huang","orcid":"https://orcid.org/0000-0003-3825-2230"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongfeng Huang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3825-2230","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100684571","display_name":"Yu\u2010Jin Zhang","orcid":"https://orcid.org/0000-0002-2372-1180"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu-Jin Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2372-1180","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.0844,"has_fulltext":false,"cited_by_count":99,"citation_normalized_percentile":{"value":0.96324815,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"26","issue":"4","first_page":"627","last_page":"631"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/steganalysis","display_name":"Steganalysis","score":0.9814741611480713},{"id":"https://openalex.org/keywords/steganography","display_name":"Steganography","score":0.8723338842391968},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.8287976980209351},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8060148358345032},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6742455363273621},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5432648658752441},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5166597366333008},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5086265802383423},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.49026206135749817},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4166281223297119},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3591231107711792},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.24612683057785034},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.21679231524467468}],"concepts":[{"id":"https://openalex.org/C107368093","wikidata":"https://www.wikidata.org/wiki/Q448176","display_name":"Steganalysis","level":4,"score":0.9814741611480713},{"id":"https://openalex.org/C108801101","wikidata":"https://www.wikidata.org/wiki/Q15032","display_name":"Steganography","level":3,"score":0.8723338842391968},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8287976980209351},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8060148358345032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6742455363273621},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5432648658752441},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5166597366333008},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5086265802383423},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.49026206135749817},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4166281223297119},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3591231107711792},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.24612683057785034},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.21679231524467468},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2019.2902095","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2019.2902095","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8299999833106995,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G2184068523","display_name":null,"funder_award_id":"U1705261","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2199153323","display_name":null,"funder_award_id":"SQ2018YGX210002","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5652245221","display_name":null,"funder_award_id":"U1536207","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6966897851","display_name":null,"funder_award_id":"U1636113","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1878907771","https://openalex.org/W2109394932","https://openalex.org/W2141599568","https://openalex.org/W2154642048","https://openalex.org/W2169482795","https://openalex.org/W2520373190","https://openalex.org/W2618218947","https://openalex.org/W2735711430","https://openalex.org/W2893556909","https://openalex.org/W2897643764","https://openalex.org/W2963626623","https://openalex.org/W3103323619","https://openalex.org/W3181074435","https://openalex.org/W4256185450","https://openalex.org/W6680890276","https://openalex.org/W6690931222","https://openalex.org/W6738568481","https://openalex.org/W6755364076","https://openalex.org/W6786004868","https://openalex.org/W6983189337"],"related_works":["https://openalex.org/W2148973528","https://openalex.org/W2939392096","https://openalex.org/W4243922849","https://openalex.org/W2106726851","https://openalex.org/W4309385482","https://openalex.org/W2792878404","https://openalex.org/W2068740952","https://openalex.org/W1583147569","https://openalex.org/W2182496537","https://openalex.org/W3154843532"],"abstract_inverted_index":{"With":[0],"the":[1,10,14,27,71,94,119,127,133,138,145],"rapid":[2],"development":[3],"of":[4,31,35,73],"natural":[5],"language":[6],"processing":[7],"technology":[8],"in":[9,98],"past":[11],"few":[12],"years,":[13],"steganography":[15,51],"by":[16],"text":[17,84],"synthesis":[18],"has":[19],"been":[20],"greatly":[21],"developed.":[22],"These":[23],"methods":[24,55],"can":[25,148],"analyze":[26],"statistical":[28,47],"feature":[29],"distribution":[30],"a":[32,67,80,109,114,150,156],"large":[33],"number":[34],"training":[36],"samples,":[37],"and":[38,65,82,112],"then":[39],"generate":[40],"steganographic":[41,101],"texts":[42],"that":[43,144],"conform":[44],"to":[45,70,87,108,117,136],"such":[46],"distribution.":[48],"For":[49],"these":[50,99,122],"methods,":[52],"previous":[53],"steganalysis":[54,85],"show":[56,143],"unsatisfactory":[57],"detection":[58,152],"performance,":[59],"which":[60,154],"remains":[61],"an":[62],"unsolved":[63],"problem":[64],"poses":[66],"great":[68],"threat":[69],"security":[72],"cyberspace.":[74],"In":[75],"this":[76,89],"letter,":[77],"we":[78,104,131],"proposed":[79,146],"fast":[81],"efficient":[83],"method":[86],"solve":[88],"problem.":[90],"We":[91],"first":[92],"analyzed":[93],"correlations":[95,120],"between":[96,121],"words":[97],"generated":[100],"texts.":[102],"Then,":[103],"map":[105],"each":[106],"word":[107],"semantic":[110],"space":[111],"used":[113,132],"hidden":[115],"layer":[116],"extract":[118],"words.":[123],"Finally,":[124],"based":[125],"on":[126],"extracted":[128],"correlation":[129],"features,":[130],"softmax":[134],"classifier":[135],"classify":[137],"input":[139],"text.":[140],"Experimental":[141],"results":[142],"model":[147],"achieve":[149],"high":[151],"accuracy,":[153],"shows":[155],"state-of-the-art":[157],"performance.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
