{"id":"https://openalex.org/W2787852615","doi":"https://doi.org/10.1109/wifs.2017.8267668","title":"A gradient-based pixel-domain attack against SVM detection of global image manipulations","display_name":"A gradient-based pixel-domain attack against SVM detection of global image manipulations","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2787852615","doi":"https://doi.org/10.1109/wifs.2017.8267668","mag":"2787852615"},"language":"en","primary_location":{"id":"doi:10.1109/wifs.2017.8267668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wifs.2017.8267668","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100325094","display_name":"Zhi\u2010Peng Chen","orcid":"https://orcid.org/0009-0004-6030-6180"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]},{"id":"https://openalex.org/I4210119101","display_name":"Tangshan Normal University","ror":"https://ror.org/02jdm8069","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119101"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Chen","raw_affiliation_strings":["Department of Computer Science, Tangshan Normal University, Tangshan, China","Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Tangshan Normal University, Tangshan, China","institution_ids":["https://openalex.org/I4210119101"]},{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026139768","display_name":"Benedetta Tondi","orcid":"https://orcid.org/0000-0002-7518-046X"},"institutions":[{"id":"https://openalex.org/I102064193","display_name":"University of Siena","ror":"https://ror.org/01tevnk56","country_code":"IT","type":"education","lineage":["https://openalex.org/I102064193"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Benedetta Tondi","raw_affiliation_strings":["Department of Information Engineering and Mathematics, University of Siena, Siena, ITALY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Mathematics, University of Siena, Siena, ITALY","institution_ids":["https://openalex.org/I102064193"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100371548","display_name":"Xiaolong Li","orcid":"https://orcid.org/0000-0002-6111-9000"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolong Li","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007154515","display_name":"Rongrong Ni","orcid":"https://orcid.org/0000-0002-5096-8752"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongrong Ni","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100362745","display_name":"Yao Zhao","orcid":"https://orcid.org/0000-0002-8581-9554"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Zhao","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007836692","display_name":"Mauro Barni","orcid":"https://orcid.org/0000-0002-7368-0866"},"institutions":[{"id":"https://openalex.org/I102064193","display_name":"University of Siena","ror":"https://ror.org/01tevnk56","country_code":"IT","type":"education","lineage":["https://openalex.org/I102064193"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mauro Barni","raw_affiliation_strings":["Department of Information Engineering and Mathematics, University of Siena, Siena, ITALY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Mathematics, University of Siena, Siena, ITALY","institution_ids":["https://openalex.org/I102064193"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.9997000098228455,"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":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9948999881744385,"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/T11515","display_name":"Bacillus and Francisella bacterial research","score":0.932699978351593,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7919796705245972},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7253996729850769},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6855599880218506},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6603233814239502},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6342467069625854},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.5407379865646362},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5381335616111755},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5170291066169739},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45501863956451416},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3908599615097046},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09947723150253296}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7919796705245972},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7253996729850769},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6855599880218506},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6603233814239502},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6342467069625854},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.5407379865646362},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5381335616111755},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5170291066169739},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45501863956451416},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3908599615097046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09947723150253296},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wifs.2017.8267668","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wifs.2017.8267668","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Workshop on Information Forensics and Security (WIFS)","raw_type":"proceedings-article"},{"id":"pmh:oai:usiena-air.unisi.it:11365/1127177","is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/document/8267668","pdf_url":null,"source":{"id":"https://openalex.org/S4377196319","display_name":"Use Siena air (University of Siena)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I102064193","host_organization_name":"University of Siena","host_organization_lineage":["https://openalex.org/I102064193"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W9657784","https://openalex.org/W385466589","https://openalex.org/W1176008057","https://openalex.org/W1515158941","https://openalex.org/W1522320082","https://openalex.org/W1965755265","https://openalex.org/W1982154664","https://openalex.org/W2000434686","https://openalex.org/W2009130368","https://openalex.org/W2029369328","https://openalex.org/W2031466562","https://openalex.org/W2044669911","https://openalex.org/W2049562124","https://openalex.org/W2054408106","https://openalex.org/W2055745001","https://openalex.org/W2071258698","https://openalex.org/W2074727269","https://openalex.org/W2081060147","https://openalex.org/W2088758928","https://openalex.org/W2095657345","https://openalex.org/W2106663508","https://openalex.org/W2133665775","https://openalex.org/W2148685281","https://openalex.org/W2151773168","https://openalex.org/W2159244236","https://openalex.org/W2167008044","https://openalex.org/W2293768274","https://openalex.org/W2296452361","https://openalex.org/W2541922885","https://openalex.org/W2962743676","https://openalex.org/W3103836116","https://openalex.org/W4233762729","https://openalex.org/W6627643800","https://openalex.org/W6630942332","https://openalex.org/W6657779969","https://openalex.org/W6682371931"],"related_works":["https://openalex.org/W121273120","https://openalex.org/W288117609","https://openalex.org/W2087874231","https://openalex.org/W2100291266","https://openalex.org/W1965781815","https://openalex.org/W2136567439","https://openalex.org/W3004377704","https://openalex.org/W2574052219","https://openalex.org/W2951312798","https://openalex.org/W2754350655"],"abstract_inverted_index":{"We":[0,106],"present":[1],"a":[2,131,135,178],"gradient-based":[3],"attack":[4,20,85,110,127,155],"against":[5,111],"SVM-based":[6],"forensic":[7],"techniques":[8],"relying":[9],"on":[10,45,86,177],"high-dimensional":[11],"SPAM":[12,35],"features.":[13],"As":[14],"opposed":[15],"to":[16,57,68,79,92,99,151,183],"prior":[17],"work,":[18],"the":[19,24,29,46,49,52,69,73,81,84,94,101,104,108,126,139,142,145,160,169,173,187],"works":[21],"directly":[22],"in":[23,129,159],"pixel":[25,32,58],"domain":[26],"even":[27],"if":[28],"relationship":[30],"between":[31,141],"values":[33],"and":[34,78,120,144],"features":[36],"can":[37],"not":[38],"be":[39],"inverted.":[40],"The":[41,154],"proposed":[42,109],"method":[43],"relies":[44],"estimation":[47],"of":[48,51,71,76,83,103,114,162],"gradient":[50,64,95],"SVM":[53,112,170],"output":[54],"with":[55,134,164,181],"respect":[56,182],"values,":[59],"however":[60],"it":[61],"departs":[62],"from":[63,149],"descent":[65],"methodology":[66],"due":[67],"necessity":[70],"preserving":[72],"integer":[74],"nature":[75],"pixels":[77],"reduce":[80,100],"effect":[82],"image":[87],"quality.":[88],"A":[89],"fast":[90],"algorithm":[91],"estimate":[93],"is":[96,156,175],"also":[97,157],"introduced":[98],"complexity":[102],"attack.":[105],"tested":[107],"detection":[113],"histogram":[115,118],"stretching,":[116],"adaptive":[117],"equalization":[119],"median":[121],"filtering.":[122],"In":[123],"all":[124],"cases":[125],"succeeded":[128],"inducing":[130],"decision":[132],"error":[133],"very":[136],"limited":[137],"distortion,":[138],"PSNR":[140],"original":[143],"attacked":[146],"images":[147],"ranging":[148],"50":[150],"70":[152],"dBs.":[153],"effective":[158],"case":[161],"attacks":[163],"Limited":[165],"Knowledge":[166],"(LK)":[167],"when":[168],"used":[171,185],"by":[172,186],"attacker":[174],"trained":[176],"different":[179],"dataset":[180],"that":[184],"analyst.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
