{"id":"https://openalex.org/W2416075718","doi":"https://doi.org/10.1145/2909827.2930798","title":"Ensemble of CNNs for Steganalysis","display_name":"Ensemble of CNNs for Steganalysis","publication_year":2016,"publication_date":"2016-06-10","ids":{"openalex":"https://openalex.org/W2416075718","doi":"https://doi.org/10.1145/2909827.2930798","mag":"2416075718"},"language":"en","primary_location":{"id":"doi:10.1145/2909827.2930798","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2909827.2930798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security","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/A5031790556","display_name":"Guanshuo Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guanshuo Xu","raw_affiliation_strings":["New Jersey Institute of Technology, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049733139","display_name":"Hanzhou Wu","orcid":"https://orcid.org/0000-0002-1599-7232"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han-Zhou Wu","raw_affiliation_strings":["Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112253415","display_name":"Yun Q. Shi","orcid":"https://orcid.org/0009-0007-4038-0430"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yun Q.. Shi","raw_affiliation_strings":["New Jersey Institute of Technology, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.1034,"has_fulltext":false,"cited_by_count":142,"citation_normalized_percentile":{"value":0.98955335,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"103","last_page":"107"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","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/T12357","display_name":"Digital Media Forensic Detection","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/T10388","display_name":"Advanced Steganography and Watermarking Techniques","score":0.9995999932289124,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9957000017166138,"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":0.8360821008682251},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7998354434967041},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7865623235702515},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.7422881722450256},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7153226137161255},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7013628482818604},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6140106916427612},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6009727716445923},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5218652486801147},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.465539813041687},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45236724615097046},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4356255531311035},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.43470197916030884},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.41949376463890076},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41190004348754883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3291093111038208},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3230772018432617},{"id":"https://openalex.org/keywords/steganography","display_name":"Steganography","score":0.14322826266288757},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12733212113380432}],"concepts":[{"id":"https://openalex.org/C107368093","wikidata":"https://www.wikidata.org/wiki/Q448176","display_name":"Steganalysis","level":4,"score":0.8360821008682251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7998354434967041},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7865623235702515},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7422881722450256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7153226137161255},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7013628482818604},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6140106916427612},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6009727716445923},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5218652486801147},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.465539813041687},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45236724615097046},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4356255531311035},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.43470197916030884},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.41949376463890076},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41190004348754883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3291093111038208},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3230772018432617},{"id":"https://openalex.org/C108801101","wikidata":"https://www.wikidata.org/wiki/Q15032","display_name":"Steganography","level":3,"score":0.14322826266288757},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12733212113380432},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2909827.2930798","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2909827.2930798","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security","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":22,"referenced_works":["https://openalex.org/W22271197","https://openalex.org/W1678356000","https://openalex.org/W1836465849","https://openalex.org/W1988790447","https://openalex.org/W2009130368","https://openalex.org/W2040299224","https://openalex.org/W2046180645","https://openalex.org/W2059019537","https://openalex.org/W2081564928","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2124664712","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2170598445","https://openalex.org/W2194775991","https://openalex.org/W2322622188","https://openalex.org/W2407561938","https://openalex.org/W2538511122","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2963682422"],"related_works":["https://openalex.org/W2022849497","https://openalex.org/W3081299480","https://openalex.org/W2407190427","https://openalex.org/W2907584218","https://openalex.org/W2919210741","https://openalex.org/W3002446410","https://openalex.org/W4390224712","https://openalex.org/W4322096758","https://openalex.org/W2748667022","https://openalex.org/W3211770882"],"abstract_inverted_index":{"There":[0],"has":[1],"been":[2,24],"growing":[3],"interest":[4],"in":[5,11,47,65,164,205],"using":[6],"convolutional":[7],"neural":[8,53],"networks":[9],"(CNNs)":[10],"the":[12,32,108,126,138,148,157,165,174,194,197],"fields":[13],"of":[14,35,96,99,107,119,141,147,173,196,208],"image":[15],"forensics":[16],"and":[17,19,44,74,129,200],"steganalysis,":[18],"some":[20,115],"promising":[21],"results":[22],"have":[23,190,211],"reported":[25],"recently.":[26],"These":[27],"works":[28],"mainly":[29],"focus":[30],"on":[31,103,179],"architectural":[33],"design":[34],"CNNs,":[36,56,97],"usually,":[37],"a":[38,85,94,104],"single":[39],"CNN":[40,88,150],"model":[41],"is":[42,50,90,101],"trained":[43,102,149],"then":[45,123],"tested":[46],"experiments.":[48],"It":[49],"known":[51],"that,":[52],"networks,":[54],"including":[55],"are":[57,122,177],"suitable":[58],"to":[59,92,132,154,161,213],"form":[60,133],"ensembles.":[61],"From":[62],"this":[63,66],"perspective,":[64],"paper,":[67],"we":[68,152],"employ":[69],"CNNs":[70,206],"as":[71],"base":[72],"learners":[73],"test":[75],"several":[76],"different":[77],"ensemble":[78,175],"strategies.":[79],"In":[80],"our":[81],"study,":[82],"at":[83,184],"first,":[84],"recently":[86],"proposed":[87],"architecture":[89],"adopted":[91],"build":[93],"group":[95],"each":[98,120],"them":[100],"random":[105],"subsample":[106],"training":[109],"dataset.":[110],"The":[111],"output":[112,209],"probabilities,":[113,210],"or":[114],"intermediate":[116,203],"feature":[117,170],"representations,":[118],"CNN,":[121],"extracted":[124],"from":[125,202],"original":[127],"data":[128],"pooled":[130],"together":[131],"new":[134],"features":[135],"ready":[136],"for":[137],"second":[139],"level":[140],"classification.":[142],"To":[143],"make":[144],"best":[145],"use":[146],"models,":[151],"manage":[153],"partially":[155],"recover":[156],"lost":[158,198],"information":[159],"due":[160],"spatial":[162],"subsampling":[163],"pooling":[166],"layers":[167],"when":[168],"forming":[169],"vectors.":[171],"Performance":[172],"methods":[176],"evaluated":[178],"BOSSbase":[180],"by":[181],"detecting":[182],"S-UNIWARD":[183],"0.4":[185],"bpp":[186],"embedding":[187],"rate.":[188],"Results":[189],"indicated":[191],"that":[192],"both":[193],"recovery":[195],"information,":[199],"learning":[201],"representation":[204],"instead":[207],"led":[212],"performance":[214],"improvement.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":26},{"year":2019,"cited_by_count":23},{"year":2018,"cited_by_count":21},{"year":2017,"cited_by_count":21},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-11T18:08:03.149640","created_date":"2025-10-10T00:00:00"}
