{"id":"https://openalex.org/W4281677189","doi":"https://doi.org/10.1142/s0218001422540167","title":"A Keypoint-Based and Block-Based Fusion Method for Image Copy\u2013Move Forgery Detection","display_name":"A Keypoint-Based and Block-Based Fusion Method for Image Copy\u2013Move Forgery Detection","publication_year":2022,"publication_date":"2022-06-04","ids":{"openalex":"https://openalex.org/W4281677189","doi":"https://doi.org/10.1142/s0218001422540167"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001422540167","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001422540167","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","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/A5004052815","display_name":"Junliu Zhong","orcid":"https://orcid.org/0000-0001-7918-6650"},"institutions":[{"id":"https://openalex.org/I4210129938","display_name":"Guangzhou Maritime College","ror":"https://ror.org/03kzcrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210129938"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun-Liu Zhong","raw_affiliation_strings":["Department of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, P. R. China","institution_ids":["https://openalex.org/I4210129938"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049347899","display_name":"Yanfen Gan","orcid":null},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan-Fen Gan","raw_affiliation_strings":["School of Computer Science, South China Business College, Guangdong University of Foreign Studies, Guangzhou 510545, P. R. China"],"raw_orcid":"https://orcid.org/0000-0001-7079-2726","affiliations":[{"raw_affiliation_string":"School of Computer Science, South China Business College, Guangdong University of Foreign Studies, Guangzhou 510545, P. R. China","institution_ids":["https://openalex.org/I186272606"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102847882","display_name":"Caifeng Zou","orcid":"https://orcid.org/0000-0001-7842-0310"},"institutions":[{"id":"https://openalex.org/I4210129938","display_name":"Guangzhou Maritime College","ror":"https://ror.org/03kzcrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210129938"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cai-Feng Zou","raw_affiliation_strings":["Department of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, P. R. China","institution_ids":["https://openalex.org/I4210129938"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2031,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.46271894,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"36","issue":"10","first_page":null,"last_page":null},"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/T13877","display_name":"Law in Society and Culture","score":0.9760000109672546,"subfield":{"id":"https://openalex.org/subfields/3308","display_name":"Law"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13114","display_name":"Image Processing Techniques and Applications","score":0.9488999843597412,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6829231977462769},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.679267406463623},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6195157766342163},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6190991401672363},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6009551882743835},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.570878803730011},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4759276211261749},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4420029819011688},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39913538098335266},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30910784006118774}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6829231977462769},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.679267406463623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6195157766342163},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6190991401672363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6009551882743835},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.570878803730011},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4759276211261749},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4420029819011688},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39913538098335266},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30910784006118774},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001422540167","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001422540167","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8904146925","display_name":null,"funder_award_id":"2020A151501783 (2020A1515010700)","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1490753124","https://openalex.org/W1570958768","https://openalex.org/W1586681179","https://openalex.org/W1781863451","https://openalex.org/W1978093332","https://openalex.org/W2022436565","https://openalex.org/W2052950171","https://openalex.org/W2067769652","https://openalex.org/W2086407934","https://openalex.org/W2118613125","https://openalex.org/W2149073238","https://openalex.org/W2154651279","https://openalex.org/W2162963619","https://openalex.org/W2179256553","https://openalex.org/W2543157448","https://openalex.org/W2572561073","https://openalex.org/W2895129173","https://openalex.org/W2897180259","https://openalex.org/W2897248782","https://openalex.org/W2993563321","https://openalex.org/W3112375996","https://openalex.org/W3136017287","https://openalex.org/W3196464730"],"related_works":["https://openalex.org/W3034955165","https://openalex.org/W2094920358","https://openalex.org/W2041448692","https://openalex.org/W2247121321","https://openalex.org/W2391926582","https://openalex.org/W2087391438","https://openalex.org/W1966831329","https://openalex.org/W2316074893","https://openalex.org/W2020188645","https://openalex.org/W2068840890"],"abstract_inverted_index":{"Currently,":[0],"image":[1,22,27,38,210],"copy\u2013move":[2,23,28,211],"forgery":[3,24,29,192,203],"issues":[4],"have":[5,16],"brought":[6],"increasingly":[7],"serious":[8],"problems":[9],"to":[10,19,37,88,100,170,183,200,215,244],"the":[11,21,48,71,78,90,95,102,112,125,130,136,141,146,154,158,163,168,172,190,195,202,217,220,225,235,241,252],"social":[12],"fields.":[13],"Many":[14],"research":[15],"been":[17],"devoted":[18],"address":[20],"issues.":[25],"However,":[26],"detection":[30,45,80,156],"(CMFD)":[31],"is":[32,86,98,181],"still":[33],"a":[34,43,82],"challenging":[35],"problem":[36],"forensics.":[39],"This":[40],"paper":[41],"proposes":[42],"coarse-to-fine":[44],"method":[46,58,223,238],"fusing":[47],"superiorities":[49],"of":[50,63,74,121,135,148,175],"both":[51],"keypoint-based":[52,64],"and":[53,66,93,118,187,208,224],"block-based":[54,75,122,159,173],"methods.":[55,76,228,255],"The":[56,106,205,229],"fusion":[57,222,237],"gets":[59],"good":[60,67],"geometric":[61],"invariances":[62],"methods":[65],"matching":[68],"robustness":[69],"with":[70],"invariant":[72],"moment":[73],"In":[77,153],"coarse":[79],"stage,":[81,157],"robust":[83],"SIFT":[84],"descriptor":[85],"used":[87,214],"extract":[89,171],"candidate":[91],"keypoints,":[92],"then":[94],"2NN":[96],"test":[97],"applied":[99],"match":[101],"suspicious":[103],"keypoint":[104,138],"couples.":[105,128],"proposed":[107,221,236],"three-pass":[108],"filtering":[109],"relying":[110,161],"on":[111,162],"Euclidean":[113],"distance,":[114],"scaling":[115,131,142,164,177],"coefficient":[116,120,132,143,165],"ratio,":[117],"correlation":[119],"features,":[123],"remove":[124],"false-positive":[126],"outlier":[127],"Finally,":[129,194],"ratio":[133,144,166],"statistics":[134],"remaining":[137],"couples":[139],"get":[140],"between":[145,219],"size":[147],"copied/pasted":[149],"or":[150],"pasted/copied":[151],"snippets.":[152,193],"fine":[155,191],"thought":[160],"uses":[167],"DAFMT":[169],"features":[174,186],"multiple":[176],"levels.":[178],"Subsequently,":[179],"LSH":[180],"presented":[182,199],"classify":[184],"block":[185],"finally":[188],"indicate":[189,201],"morphological":[196],"operations":[197],"are":[198,213],"accurately.":[204],"benchmark":[206],"IMD":[207],"CoMoFoD":[209],"datasets":[212],"measure":[216],"performances":[218,243],"state-of-the-art":[226,254],"CMFD":[227],"numerous":[230],"experimental":[231],"results":[232],"demonstrate":[233],"that":[234],"achieves":[239],"nearly":[240],"best":[242],"resist":[245],"various":[246],"attacks,":[247,250],"especially":[248],"large-scaling":[249],"among":[251],"compared":[253]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}