{"id":"https://openalex.org/W4400810139","doi":"https://doi.org/10.1109/lsp.2024.3431440","title":"Attention-Based Dual-Domain Fusion Network for Median Filtering Forensics","display_name":"Attention-Based Dual-Domain Fusion Network for Median Filtering Forensics","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400810139","doi":"https://doi.org/10.1109/lsp.2024.3431440"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2024.3431440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2024.3431440","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/A5019614432","display_name":"Yakun Niu","orcid":"https://orcid.org/0000-0002-2793-2823"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yakun Niu","raw_affiliation_strings":["Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China"],"raw_orcid":"https://orcid.org/0000-0002-2793-2823","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114929272","display_name":"Lei Tan","orcid":"https://orcid.org/0009-0001-8952-821X"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Tan","raw_affiliation_strings":["Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China"],"raw_orcid":"https://orcid.org/0009-0001-8952-821X","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100637600","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0002-8092-3459"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China"],"raw_orcid":"https://orcid.org/0000-0002-8092-3459","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yingjian Chen","orcid":"https://orcid.org/0009-0004-1359-6998"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingjian Chen","raw_affiliation_strings":["Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China"],"raw_orcid":"https://orcid.org/0009-0004-1359-6998","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044310348","display_name":"Xianyu Zuo","orcid":"https://orcid.org/0000-0002-5675-2362"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianyu Zuo","raw_affiliation_strings":["Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China"],"raw_orcid":"https://orcid.org/0000-0002-5675-2362","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I173899330"],"apc_list":null,"apc_paid":null,"fwci":0.832,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.70535862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"31","issue":null,"first_page":"1860","last_page":"1864"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9940999746322632,"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":0.9940999746322632,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9901999831199646,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9742000102996826,"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/computer-science","display_name":"Computer science","score":0.6910524368286133},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6342807412147522},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5239627361297607},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.47136130928993225},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4394628405570984},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4250231981277466},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34046271443367004},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17107322812080383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6910524368286133},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6342807412147522},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5239627361297607},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.47136130928993225},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4394628405570984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4250231981277466},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34046271443367004},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17107322812080383},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2024.3431440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2024.3431440","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":[],"awards":[{"id":"https://openalex.org/G2266594779","display_name":null,"funder_award_id":"62202141","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W22271197","https://openalex.org/W1866230716","https://openalex.org/W1973629749","https://openalex.org/W1985613413","https://openalex.org/W2019716459","https://openalex.org/W2037974317","https://openalex.org/W2055745001","https://openalex.org/W2148685281","https://openalex.org/W2159244236","https://openalex.org/W2160921722","https://openalex.org/W2194775991","https://openalex.org/W2407561938","https://openalex.org/W2520762063","https://openalex.org/W2580671710","https://openalex.org/W2774322245","https://openalex.org/W2789746591","https://openalex.org/W2798117183","https://openalex.org/W2888895195","https://openalex.org/W2900465856","https://openalex.org/W2907863985","https://openalex.org/W3000214932","https://openalex.org/W3003260905","https://openalex.org/W3096831136","https://openalex.org/W3206435859","https://openalex.org/W3212937108","https://openalex.org/W4206945148","https://openalex.org/W4212841783","https://openalex.org/W4360979411","https://openalex.org/W4367054335","https://openalex.org/W4389794003"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W4285447065","https://openalex.org/W2952466936","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W2515288625","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W2031175860","https://openalex.org/W2152662039"],"abstract_inverted_index":{"Median":[0],"filtering":[1],"forensics":[2],"has":[3],"attracted":[4],"much":[5],"attention":[6,115],"in":[7,35,59,134],"recent":[8],"years.":[9],"However,":[10],"most":[11],"existing":[12],"methods":[13,133],"exploit":[14],"either":[15],"spatial":[16,53,66],"domain":[17,20,54,57],"or":[18],"frequency":[19,56,83,100],"features,":[21],"while":[22],"neglect":[23],"the":[24,65,77,82,85,99,104,108,127,131],"correlation":[25],"between":[26],"them.":[27],"Moreover,":[28],"they":[29],"often":[30],"suffer":[31],"from":[32,107],"inadequate":[33],"generalization":[34,139],"cross-database":[36],"and":[37,55,138],"cross-parameter.":[38],"To":[39],"solve":[40],"these":[41],"problems,":[42],"we":[43,68],"propose":[44],"an":[45,114],"attention-based":[46],"dual-domain":[47],"fusion":[48],"network":[49,96],"(DDFNet)":[50],"to":[51,97,117],"fuse":[52],"features":[58,105],"a":[60,94],"mutually":[61],"complementary":[62,121],"way.":[63],"In":[64,81],"domain,":[67,84],"use":[69],"four":[70],"types":[71],"of":[72,136],"modules":[73],"aimed":[74],"at":[75],"extracting":[76],"pixel":[78],"residual":[79,95,101],"features.":[80,102],"discrete":[86],"cosine":[87],"transform":[88],"(DCT)":[89],"coefficients":[90],"are":[91,111],"fed":[92],"into":[93],"capture":[98],"Finally,":[103],"obtained":[106],"two":[109],"domains":[110],"fused":[112],"by":[113],"module":[116],"mine":[118],"their":[119],"latent":[120],"relationships.":[122],"Extensive":[123],"experiments":[124],"demonstrate":[125],"that":[126],"proposed":[128],"DDFNet":[129],"outperforms":[130],"state-of-the-art":[132],"terms":[135],"robustness":[137],"capacity.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}