{"id":"https://openalex.org/W4416649914","doi":"https://doi.org/10.1109/access.2025.3636816","title":"Robust Audio Copy-Move Forgery Detection Using Deep Keypoint Features on High-Resolution Texture Images","display_name":"Robust Audio Copy-Move Forgery Detection Using Deep Keypoint Features on High-Resolution Texture Images","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416649914","doi":"https://doi.org/10.1109/access.2025.3636816"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3636816","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3636816","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3636816","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106442276","display_name":"Arda \u00dcst\u00fcb\u0131o\u011flu","orcid":"https://orcid.org/0000-0002-8656-8697"},"institutions":[{"id":"https://openalex.org/I1329879541","display_name":"Avrasya University","ror":"https://ror.org/00pv8s374","country_code":"TR","type":"education","lineage":["https://openalex.org/I1329879541"]},{"id":"https://openalex.org/I162720556","display_name":"Karadeniz Technical University","ror":"https://ror.org/03z8fyr40","country_code":"TR","type":"education","lineage":["https://openalex.org/I162720556"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Arda \u00dcst\u00fcb\u0131o\u011flu","raw_affiliation_strings":["Computer Engineering Department, Trabzon University, Trabzon, Türkiye","Computer Engineering Department, Trabzon University, Turkey"],"raw_orcid":"https://orcid.org/0000-0002-8656-8697","affiliations":[{"raw_affiliation_string":"Computer Engineering Department, Trabzon University, Trabzon, Türkiye","institution_ids":["https://openalex.org/I162720556"]},{"raw_affiliation_string":"Computer Engineering Department, Trabzon University, Turkey","institution_ids":["https://openalex.org/I1329879541"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5106442276"],"corresponding_institution_ids":["https://openalex.org/I1329879541","https://openalex.org/I162720556"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.0875,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.83834456,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"13","issue":null,"first_page":"202228","last_page":"202252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9878000020980835,"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":0.9878000020980835,"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":0.0027000000700354576,"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/T12034","display_name":"Digital and Cyber Forensics","score":0.002400000113993883,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/spectrogram","display_name":"Spectrogram","score":0.703499972820282},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5924000144004822},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5573999881744385},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4641999900341034},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4636000096797943},{"id":"https://openalex.org/keywords/audio-signal","display_name":"Audio signal","score":0.4153999984264374},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.3986000120639801},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.375},{"id":"https://openalex.org/keywords/texture","display_name":"Texture (cosmology)","score":0.3610000014305115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7738999724388123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7605000138282776},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.703499972820282},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5924000144004822},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5717999935150146},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5573999881744385},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4641999900341034},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4636000096797943},{"id":"https://openalex.org/C64922751","wikidata":"https://www.wikidata.org/wiki/Q4650799","display_name":"Audio signal","level":3,"score":0.4153999984264374},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3986000120639801},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.375},{"id":"https://openalex.org/C2781195486","wikidata":"https://www.wikidata.org/wiki/Q289436","display_name":"Texture (cosmology)","level":3,"score":0.3610000014305115},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32339999079704285},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C127220857","wikidata":"https://www.wikidata.org/wiki/Q2719318","display_name":"Audio signal processing","level":4,"score":0.3091000020503998},{"id":"https://openalex.org/C136943445","wikidata":"https://www.wikidata.org/wiki/Q1970240","display_name":"Histogram equalization","level":4,"score":0.3075999915599823},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.30720001459121704},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C150817343","wikidata":"https://www.wikidata.org/wiki/Q875932","display_name":"Digital watermarking","level":3,"score":0.27619999647140503},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.26260000467300415},{"id":"https://openalex.org/C87687168","wikidata":"https://www.wikidata.org/wiki/Q173114","display_name":"Digital audio","level":4,"score":0.2581000030040741},{"id":"https://openalex.org/C84418412","wikidata":"https://www.wikidata.org/wiki/Q3246940","display_name":"Digital forensics","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C128422554","wikidata":"https://www.wikidata.org/wiki/Q20077126","display_name":"Sound recording and reproduction","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3636816","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3636816","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6271eca6a1544271b8c65f06427c4b63","is_oa":true,"landing_page_url":"https://doaj.org/article/6271eca6a1544271b8c65f06427c4b63","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 202228-202252 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3636816","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3636816","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Audio":[0],"Copy-Move":[1],"Forgery":[2],"(ACMF)":[3],"is":[4,65,90,176],"a":[5,59,93,97,154,226],"critical":[6],"security":[7],"problem":[8],"that":[9],"compromises":[10],"the":[11,30,34,38,77,87,116,120,127,136,140,146,157,163,167,173,192,203],"integrity":[12],"of":[13,33,40,156,169],"digital":[14],"audio":[15,88,174],"recordings":[16],"and":[17,48,69,103,122,139,166,187,199,237],"complicates":[18],"forensic":[19,41],"investigations.":[20],"Traditional":[21],"methods":[22],"based":[23,80,99,110],"on":[24,184,209],"time-frequency":[25],"analyses":[26],"cannot":[27,52],"reliably":[28],"detect":[29],"forged":[31],"parts":[32],"audio,":[35],"especially":[36],"in":[37,172,191],"face":[39],"attacks":[42],"such":[43],"as":[44,50,153],"noise":[45,216],"addition,":[46],"compression":[47],"filtering,":[49],"they":[51],"preserve":[53],"high-resolution":[54,94],"details.":[55],"In":[56],"this":[57,84],"study,":[58],"new":[60],"attack-resistant":[61],"ACMF":[62,189],"detection":[63,231],"method":[64,138,180,224],"presented":[66],"by":[67,126,135,145],"extracting":[68],"matching":[70],"keypoints":[71,121,132],"from":[72,115],"super-resolution":[73],"texture":[74,111],"images":[75],"using":[76],"deep":[78],"learning":[79],"ALIKE":[81,128],"method.":[82],"For":[83],"purpose,":[85],"firstly,":[86],"signal":[89],"converted":[91],"to":[92,234],"spectrogram":[95,117],"with":[96,229],"Coupled-PHCA":[98],"technique;":[100],"After":[101],"denoising":[102],"L":[104],"channel":[105],"histogram":[106],"equalization":[107],"steps,":[108],"LBP":[109],"maps":[112],"are":[113,124,133,143,160],"obtained":[114,131,152],"image.":[118],"Then,":[119],"descriptors":[123],"extracted":[125],"network.":[129],"The":[130,150,179,222],"matched":[134],"g2NN":[137],"false":[141],"matches":[142,151],"eliminated":[144],"proposed":[147,223],"filtering":[148,158],"algorithm.":[149],"result":[155],"algorithm":[159],"projected":[161],"onto":[162],"time":[164],"axis":[165],"localization":[168],"repeated":[170],"segments":[171],"file":[175],"precisely":[177],"determined.":[178],"has":[181],"been":[182],"tested":[183],"TIMIT-ACMF,":[185],"Arabic-ACMF":[186],"Turkish":[188],"datasets;":[190],"non-attack":[193],"case,":[194],"it":[195],"achieved":[196],"92.1%,":[197],"93.0%":[198],"94.8%":[200],"accuracy,":[201],"respectively;":[202],"accuracy":[204],"was":[205],"maintained":[206],"above":[207],"90%":[208],"all":[210],"datasets":[211],"under":[212],"30":[213],"dB":[214],"Gaussian":[215],"or":[217],"64":[218],"kbps":[219],"MP3":[220],"compression.":[221],"showed":[225],"clear":[227],"superiority":[228],"higher":[230],"rate":[232],"compared":[233],"window-based,":[235],"VAD-based":[236],"spectrogram-based":[238],"approaches.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-11-25T00:00:00"}