{"id":"https://openalex.org/W3134316763","doi":"https://doi.org/10.1109/wifs49906.2020.9360885","title":"The Syndrome-Trellis Sampler for Generative Steganography","display_name":"The Syndrome-Trellis Sampler for Generative Steganography","publication_year":2020,"publication_date":"2020-12-06","ids":{"openalex":"https://openalex.org/W3134316763","doi":"https://doi.org/10.1109/wifs49906.2020.9360885","mag":"3134316763"},"language":"en","primary_location":{"id":"doi:10.1109/wifs49906.2020.9360885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wifs49906.2020.9360885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Workshop on Information Forensics and Security (WIFS)","raw_type":"proceedings-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/A5072517256","display_name":"Tamio-Vesa Nakajima","orcid":"https://orcid.org/0000-0003-3684-9412"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Tamio-Vesa Nakajima","raw_affiliation_strings":["University College University of Oxford, Oxford, UK"],"affiliations":[{"raw_affiliation_string":"University College University of Oxford, Oxford, UK","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111728681","display_name":"Andrew D. Ker","orcid":"https://orcid.org/0000-0002-1154-3305"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Andrew D. Ker","raw_affiliation_strings":["Department of Computer Science, University of Oxford, Oxford, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Oxford, Oxford, UK","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5072517256"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.44231028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11017","display_name":"Chaos-based Image/Signal Encryption","score":0.995199978351593,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9947999715805054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/steganography","display_name":"Steganography","score":0.8915690779685974},{"id":"https://openalex.org/keywords/steganalysis","display_name":"Steganalysis","score":0.7393426895141602},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6463302969932556},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.5702458024024963},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5522319674491882},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5329293608665466},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5021572113037109},{"id":"https://openalex.org/keywords/trellis","display_name":"Trellis (graph)","score":0.49628931283950806},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.452825129032135},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.43744492530822754},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32118505239486694},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30417168140411377},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.20314964652061462},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1858232021331787},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16174840927124023},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.15279114246368408}],"concepts":[{"id":"https://openalex.org/C108801101","wikidata":"https://www.wikidata.org/wiki/Q15032","display_name":"Steganography","level":3,"score":0.8915690779685974},{"id":"https://openalex.org/C107368093","wikidata":"https://www.wikidata.org/wiki/Q448176","display_name":"Steganalysis","level":4,"score":0.7393426895141602},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6463302969932556},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.5702458024024963},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5522319674491882},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5329293608665466},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5021572113037109},{"id":"https://openalex.org/C2781235587","wikidata":"https://www.wikidata.org/wiki/Q7838009","display_name":"Trellis (graph)","level":3,"score":0.49628931283950806},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.452825129032135},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.43744492530822754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32118505239486694},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30417168140411377},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.20314964652061462},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1858232021331787},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16174840927124023},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.15279114246368408},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wifs49906.2020.9360885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wifs49906.2020.9360885","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Workshop on Information Forensics and Security (WIFS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W32140476","https://openalex.org/W1512190422","https://openalex.org/W2012300482","https://openalex.org/W2074754392","https://openalex.org/W2079485208","https://openalex.org/W2134527668","https://openalex.org/W2154026545","https://openalex.org/W2416746444","https://openalex.org/W2809596579","https://openalex.org/W2893556909","https://openalex.org/W2971140743","https://openalex.org/W6601359535","https://openalex.org/W6669040815"],"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/W2068740952","https://openalex.org/W2182496537","https://openalex.org/W2792878404","https://openalex.org/W3154843532","https://openalex.org/W1965039524"],"abstract_inverted_index":{"We":[0,68,110],"adapt":[1],"the":[2,24,75,83,87,92,98,101,105,118],"Syndrome-Trellis":[3],"Code":[4],"algorithm":[5],"to":[6,19,55,58,72,80,114],"generative":[7],"steganography,":[8],"giving":[9],"a":[10,15,123],"method":[11,54,76],"for":[12,33],"sampling":[13],"from":[14,91,126],"specified":[16],"distribution":[17,89],"subject":[18],"linear":[20],"constraints.":[21],"This":[22],"allows":[23],"use":[25],"of":[26,100,104],"syndrome":[27,106],"codes,":[28],"popular":[29],"in":[30,117,122],"cover-modification":[31],"methods,":[32],"cover-generation":[34],"steganography.":[35,128],"The":[36,94],"SyndromeTrellis":[37],"Sampler":[38],"works":[39],"directly":[40],"on":[41],"independent":[42],"and":[43,46,79],"Markov-chain":[44],"distributions,":[45],"can":[47,62],"be":[48,63],"plugged":[49],"into":[50,65],"an":[51],"existing":[52],"STC-based":[53],"extend":[56],"it":[57],"Gibbs":[59],"fields":[60],"that":[61,74,97],"decomposed":[64],"conditionally-independent":[66],"sublattices.":[67],"give":[69],"some":[70],"experiments":[71],"show":[73,96,112],"is":[77,108],"correct,":[78],"quantify":[81],"how":[82,113],"payload":[84],"condition":[85],"forces":[86],"sampled":[88],"away":[90],"target.":[93],"results":[95],"secrecy":[99],"parity-check":[102],"matrix":[103],"code":[107],"important.":[109],"also":[111],"exploit":[115],"sparsity":[116],"conditional":[119],"cover":[120],"distribution,":[121],"simple":[124],"example":[125],"linguistic":[127]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
