{"id":"https://openalex.org/W3003940567","doi":"https://doi.org/10.1109/access.2020.2970735","title":"Deep Learning Local Descriptor for Image Splicing Detection and Localization","display_name":"Deep Learning Local Descriptor for Image Splicing Detection and Localization","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3003940567","doi":"https://doi.org/10.1109/access.2020.2970735","mag":"3003940567"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2970735","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2970735","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08977568.pdf","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://ieeexplore.ieee.org/ielx7/6287639/8948470/08977568.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101423126","display_name":"Yuan Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuan Rao","raw_affiliation_strings":["School of Electronic and Information Technology, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Technology, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039213686","display_name":"Jiangqun Ni","orcid":"https://orcid.org/0000-0002-7520-9031"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangqun Ni","raw_affiliation_strings":["Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China","School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075054710","display_name":"Huimin Zhao","orcid":"https://orcid.org/0000-0002-6877-2002"},"institutions":[{"id":"https://openalex.org/I4210122543","display_name":"Guangdong Polytechnic Normal University","ror":"https://ror.org/02pcb5m77","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huimin Zhao","raw_affiliation_strings":["School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I4210122543"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101423126"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":5.7991,"has_fulltext":true,"cited_by_count":97,"citation_normalized_percentile":{"value":0.97048919,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"8","issue":null,"first_page":"25611","last_page":"25625"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":1.0,"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":1.0,"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":0.9593999981880188,"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":0.9488999843597412,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7380138635635376},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6447820663452148},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.560255229473114},{"id":"https://openalex.org/keywords/rna-splicing","display_name":"RNA splicing","score":0.5230354070663452},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5160591006278992},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4692516326904297},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4554634988307953},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43426191806793213},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4210456609725952},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.11545106768608093},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.0952201783657074},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.09450235962867737}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7380138635635376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6447820663452148},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.560255229473114},{"id":"https://openalex.org/C54458228","wikidata":"https://www.wikidata.org/wiki/Q237218","display_name":"RNA splicing","level":4,"score":0.5230354070663452},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5160591006278992},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4692516326904297},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4554634988307953},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43426191806793213},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4210456609725952},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.11545106768608093},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0952201783657074},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.09450235962867737},{"id":"https://openalex.org/C67705224","wikidata":"https://www.wikidata.org/wiki/Q11053","display_name":"RNA","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2970735","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2970735","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08977568.pdf","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:1f4e2009ad5e46fc82a52a46aa451856","is_oa":true,"landing_page_url":"https://doaj.org/article/1f4e2009ad5e46fc82a52a46aa451856","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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 8, Pp 25611-25625 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2970735","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2970735","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08977568.pdf","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":[{"score":0.7099999785423279,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1477544716","display_name":null,"funder_award_id":"Guangdong","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1780022192","display_name":null,"funder_award_id":"2017KCXTD021","funder_id":"https://openalex.org/F4320326279","funder_display_name":"Department of Education of Guangdong Province"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G253158607","display_name":null,"funder_award_id":"U1736215","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4020255992","display_name":null,"funder_award_id":"Project","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G434869240","display_name":null,"funder_award_id":"201707","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4739086791","display_name":null,"funder_award_id":"2017KCXTD021","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G551293056","display_name":null,"funder_award_id":"61772573","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5983660024","display_name":null,"funder_award_id":"2017070","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7810175455","display_name":null,"funder_award_id":"201804","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G848105813","display_name":null,"funder_award_id":"KCXTD021","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"},{"id":"https://openalex.org/F4320326279","display_name":"Department of Education of Guangdong Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3003940567.pdf","grobid_xml":"https://content.openalex.org/works/W3003940567.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W1665214252","https://openalex.org/W1745334888","https://openalex.org/W1836465849","https://openalex.org/W1971111339","https://openalex.org/W1982479097","https://openalex.org/W1983586459","https://openalex.org/W1994743750","https://openalex.org/W2009130368","https://openalex.org/W2033206719","https://openalex.org/W2034556443","https://openalex.org/W2046686077","https://openalex.org/W2061395358","https://openalex.org/W2068734928","https://openalex.org/W2071794886","https://openalex.org/W2089818991","https://openalex.org/W2108069432","https://openalex.org/W2127589108","https://openalex.org/W2142694135","https://openalex.org/W2144172034","https://openalex.org/W2153635508","https://openalex.org/W2155893237","https://openalex.org/W2161236525","https://openalex.org/W2163605009","https://openalex.org/W2170343586","https://openalex.org/W2171590421","https://openalex.org/W2194775991","https://openalex.org/W2281375574","https://openalex.org/W2541922885","https://openalex.org/W2543923489","https://openalex.org/W2572561073","https://openalex.org/W2574852830","https://openalex.org/W2580690807","https://openalex.org/W2599632008","https://openalex.org/W2603123944","https://openalex.org/W2605252717","https://openalex.org/W2621048556","https://openalex.org/W2752015292","https://openalex.org/W2802701183","https://openalex.org/W2892187710","https://openalex.org/W2902301960","https://openalex.org/W2911605501","https://openalex.org/W2949117887","https://openalex.org/W2952793010","https://openalex.org/W2963635628","https://openalex.org/W2964146055","https://openalex.org/W4298289240","https://openalex.org/W6637242042","https://openalex.org/W6637568146","https://openalex.org/W6638667902","https://openalex.org/W6681239517","https://openalex.org/W6684191040","https://openalex.org/W6736232099"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4224241862","https://openalex.org/W2889547007","https://openalex.org/W1510729279","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W4320729701","https://openalex.org/W4254103348","https://openalex.org/W3210378990","https://openalex.org/W3034745255"],"abstract_inverted_index":{"In":[0,90],"this":[1],"paper,":[2],"a":[3,126,167],"novel":[4],"image":[5,71,86,169,195,207,244],"splicing":[6,87,196,208,245],"detection":[7,88,197,246],"and":[8,40,73,77,122,144,247],"localization":[9,209,248],"scheme":[10,210,236],"is":[11,20,38,64,82,123,186,211],"proposed":[12,61,158,233],"based":[13,235],"on":[14,201,226],"the":[15,48,60,68,75,92,95,106,132,137,141,153,157,162,172,190,202,216,232],"local":[16,36,175],"feature":[17,179],"descriptor":[18,37],"which":[19,31,81],"learned":[21,138],"by":[22,171,214],"deep":[23],"convolutional":[24,97],"neural":[25],"network":[26],"(CNN).":[27],"A":[28],"two-branch":[29],"CNN,":[30],"serves":[32],"as":[33,183],"an":[34,102,177,206],"expressive":[35,78],"presented":[39],"applied":[41],"to":[42,66,130,150,188],"automatically":[43],"learn":[44],"hierarchical":[45],"representations":[46],"from":[47],"input":[49],"RGB":[50],"color":[51],"or":[52],"grayscale":[53],"test":[54,168],"images.":[55],"The":[56],"first":[57,96],"layer":[58,98],"of":[59,70,94,105,114,156],"CNN":[62,159,204,234],"model":[63,120],"used":[65,111],"suppress":[67],"effects":[69],"contents":[72],"extract":[74],"diverse":[76],"residual":[79,115],"features,":[80],"deliberately":[83],"designed":[84],"for":[85,136,166,194],"applications.":[89],"specific,":[91],"kernels":[93],"are":[99,148],"initialized":[100],"with":[101,198],"optimized":[103],"combination":[104],"30":[107],"linear":[108],"high-pass":[109,133],"filters":[110],"in":[112,117,243,252],"calculation":[113],"maps":[116],"spatial":[118],"rich":[119],"(SRM),":[121],"fine-tuned":[124],"through":[125],"constrained":[127],"learning":[128],"strategy":[129],"retain":[131],"filtering":[134],"properties":[135],"kernels.":[139],"Both":[140],"contrastive":[142],"loss":[143,147],"cross":[145],"entropy":[146],"utilized":[149],"jointly":[151],"improve":[152],"generalization":[154],"ability":[155],"model.":[160],"With":[161],"block-wise":[163],"dense":[164],"features":[165,193],"extracted":[170],"pre-trained":[173,203],"CNN-based":[174],"descriptor,":[176],"effective":[178],"fusion":[180],"strategy,":[181],"known":[182],"block":[184],"pooling,":[185],"adopted":[187],"obtain":[189],"final":[191],"discriminative":[192],"SVM.":[199],"Based":[200],"model,":[205],"further":[212],"developed":[213],"incorporating":[215],"fully":[217],"connected":[218],"conditional":[219],"random":[220],"field":[221],"(CRF).":[222],"Extensive":[223],"experimental":[224],"results":[225],"several":[227],"public":[228],"datasets":[229],"show":[230],"that":[231],"outperforms":[237],"some":[238],"state-of-the-art":[239],"methods":[240],"not":[241],"only":[242],"performance,":[249],"but":[250],"also":[251],"robustness":[253],"against":[254],"JPEG":[255],"compression.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":3}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}