{"id":"https://openalex.org/W4380028492","doi":"https://doi.org/10.3384/ecp199005","title":"Evaluation of Defense Methods Against the One-Pixel Attack on Deep Neural Networks","display_name":"Evaluation of Defense Methods Against the One-Pixel Attack on Deep Neural Networks","publication_year":2023,"publication_date":"2023-06-09","ids":{"openalex":"https://openalex.org/W4380028492","doi":"https://doi.org/10.3384/ecp199005"},"language":"en","primary_location":{"id":"doi:10.3384/ecp199005","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3384/ecp199005","pdf_url":"https://ecp.ep.liu.se/index.php/sais/article/download/717/623","source":{"id":"https://openalex.org/S4220651186","display_name":"Link\u00f6ping electronic conference proceedings","issn_l":"1650-3686","issn":["1650-3686","1650-3740"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310317096","host_organization_name":"Link\u00f6ping University Electronic Press","host_organization_lineage":["https://openalex.org/P4310317096"],"host_organization_lineage_names":["Link\u00f6ping University Electronic Press"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Link\u00f6ping Electronic Conference Proceedings","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ecp.ep.liu.se/index.php/sais/article/download/717/623","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092124067","display_name":"Victor Arvidsson","orcid":null},"institutions":[{"id":"https://openalex.org/I52719799","display_name":"Blekinge Institute of Technology","ror":"https://ror.org/0093a8w51","country_code":"SE","type":"education","lineage":["https://openalex.org/I52719799"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Victor Arvidsson","raw_affiliation_strings":["Department of Com-puter Science, Blekinge Institute of Technology, Karlskrona, Sweden","Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Com-puter Science, Blekinge Institute of Technology, Karlskrona, Sweden","institution_ids":["https://openalex.org/I52719799"]},{"raw_affiliation_string":"Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden","institution_ids":["https://openalex.org/I52719799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092124068","display_name":"Ahmad Al-Mashahedi","orcid":null},"institutions":[{"id":"https://openalex.org/I52719799","display_name":"Blekinge Institute of Technology","ror":"https://ror.org/0093a8w51","country_code":"SE","type":"education","lineage":["https://openalex.org/I52719799"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ahmad Al-Mashahedi","raw_affiliation_strings":["Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden","Department of Com-puter Science, Blekinge Institute of Technology, Karlskrona, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden","institution_ids":["https://openalex.org/I52719799"]},{"raw_affiliation_string":"Department of Com-puter Science, Blekinge Institute of Technology, Karlskrona, Sweden","institution_ids":["https://openalex.org/I52719799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009173383","display_name":"Martin Boldt","orcid":"https://orcid.org/0000-0002-9316-4842"},"institutions":[{"id":"https://openalex.org/I52719799","display_name":"Blekinge Institute of Technology","ror":"https://ror.org/0093a8w51","country_code":"SE","type":"education","lineage":["https://openalex.org/I52719799"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Martin Boldt","raw_affiliation_strings":["Department of Com-puter Science, Blekinge Institute of Technology, Karlskrona, Sweden","Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Com-puter Science, Blekinge Institute of Technology, Karlskrona, Sweden","institution_ids":["https://openalex.org/I52719799"]},{"raw_affiliation_string":"Department of Computer Science, Blekinge Institute of Technology, Karlskrona, Sweden","institution_ids":["https://openalex.org/I52719799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.104,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.22966014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"199","issue":null,"first_page":"49","last_page":"57"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9603999853134155,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11515","display_name":"Bacillus and Francisella bacterial research","score":0.9517999887466431,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7957309484481812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.780167281627655},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7762858867645264},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.7467367649078369},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6617512702941895},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6103933453559875},{"id":"https://openalex.org/keywords/gaussian-blur","display_name":"Gaussian blur","score":0.5667256116867065},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5546382069587708},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5177413821220398},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.45857149362564087},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45685699582099915},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4530389904975891},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.420681893825531},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36670491099357605},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.23336219787597656},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.17338508367538452},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.1477152705192566}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7957309484481812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.780167281627655},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7762858867645264},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7467367649078369},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6617512702941895},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6103933453559875},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.5667256116867065},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5546382069587708},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5177413821220398},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.45857149362564087},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45685699582099915},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4530389904975891},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.420681893825531},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36670491099357605},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.23336219787597656},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.17338508367538452},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.1477152705192566},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3384/ecp199005","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3384/ecp199005","pdf_url":"https://ecp.ep.liu.se/index.php/sais/article/download/717/623","source":{"id":"https://openalex.org/S4220651186","display_name":"Link\u00f6ping electronic conference proceedings","issn_l":"1650-3686","issn":["1650-3686","1650-3740"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310317096","host_organization_name":"Link\u00f6ping University Electronic Press","host_organization_lineage":["https://openalex.org/P4310317096"],"host_organization_lineage_names":["Link\u00f6ping University Electronic Press"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Link\u00f6ping Electronic Conference Proceedings","raw_type":"proceedings-article"},{"id":"pmh:oai:DiVA.org:bth-25418","is_oa":true,"landing_page_url":"http://urn.kb.se/resolve?urn=urn:nbn:se:bth-25418","pdf_url":"https://bth.diva-portal.org/smash/get/diva2:1800649/FULLTEXT01","source":{"id":"https://openalex.org/S4306401559","display_name":"KTH Publication Database DiVA (KTH Royal Institute of Technology)","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.3384/ecp199005","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3384/ecp199005","pdf_url":"https://ecp.ep.liu.se/index.php/sais/article/download/717/623","source":{"id":"https://openalex.org/S4220651186","display_name":"Link\u00f6ping electronic conference proceedings","issn_l":"1650-3686","issn":["1650-3686","1650-3740"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310317096","host_organization_name":"Link\u00f6ping University Electronic Press","host_organization_lineage":["https://openalex.org/P4310317096"],"host_organization_lineage_names":["Link\u00f6ping University Electronic Press"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Link\u00f6ping Electronic Conference Proceedings","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4380028492.pdf"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2123045220","https://openalex.org/W2180612164","https://openalex.org/W2516574342","https://openalex.org/W2607219512","https://openalex.org/W2775795276","https://openalex.org/W2809895662","https://openalex.org/W2963857521","https://openalex.org/W2992542189","https://openalex.org/W3009235743","https://openalex.org/W3089898189","https://openalex.org/W3092066931","https://openalex.org/W3103557498","https://openalex.org/W3118608800","https://openalex.org/W3186199857","https://openalex.org/W3201502160","https://openalex.org/W4291213652","https://openalex.org/W6638444622","https://openalex.org/W6785841629"],"related_works":["https://openalex.org/W3176659669","https://openalex.org/W4287115361","https://openalex.org/W2063769574","https://openalex.org/W2134831563","https://openalex.org/W2549920676","https://openalex.org/W2083966666","https://openalex.org/W2368921446","https://openalex.org/W4241340242","https://openalex.org/W2391139946","https://openalex.org/W2990717236"],"abstract_inverted_index":{"The":[0,18,60,79],"one-pixel":[1,54,170,304],"attack":[2,6,19],"is":[3,174,239,261,282,297],"an":[4,149,162],"image":[5],"method":[7,20],"for":[8,199,216,228],"creating":[9],"adversarial":[10,23,48,184],"instances":[11,24,135],"with":[12,279],"minimal":[13],"perturbations,":[14],"i.e.,":[15],"pixel":[16,34],"modification.":[17],"makes":[21],"the":[22,36,53,83,96,105,118,124,132,141,145,166,169,183,193,201,217,226,229,235,242,246,267,286,294,303],"difficult":[25],"to":[26,155,164,178,210],"detect":[27],"as":[28],"it":[29,173,223],"only":[30],"manipulates":[31],"a":[32,129,253,262,272,299],"single":[33],"in":[35,92,131],"image.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41,127],"study":[42],"four":[43],"different":[44,58],"defense":[45,61,157,238,264,301],"approaches":[46],"against":[47,180,302],"attacks,":[49],"and":[50,69,77,95,104,172,203,219,258,269],"more":[51,283],"specifically":[52],"attack,":[55,171],"over":[56],"three":[57,85],"models.":[59,205,221],"methods":[62],"used":[63,73,122],"are:":[64],"data":[65,71,110,207,256,276],"augmentation,":[66],"spatial":[67,259,280],"smoothing,":[68],"Gaussian":[70,109,275,291],"augmentation":[72,111,187,208,257,277],"during":[74,117,123,293],"both":[75,200],"training":[76,125],"testing.":[78],"empirical":[80],"experiments":[81,287],"involve":[82],"following":[84],"models:":[86],"all":[87],"convolutional":[88,97],"network":[89,91,93,99],"(CNN),":[90],"(NiN),":[94],"neural":[98],"VGG16.":[100],"Experiments":[101],"were":[102],"executed":[103],"results":[106,250],"show":[107],"that":[108,136,234,252,289],"performs":[112],"quite":[113],"poorly":[114],"when":[115],"applied":[116],"prediction":[119,295],"phase.":[120],"When":[121],"phase,":[126],"see":[128],"reduction":[130],"number":[133,194],"of":[134,168,182,195,255,274],"could":[137],"be":[138],"perturbed":[139,197],"by":[140],"NiN":[142,204,218,268],"model.":[143,231],"However,":[144,206],"CNN":[146,202,230,247],"model":[147,214,243],"shows":[148,161,189],"overall":[150,213],"significantly":[151,224],"worse":[152,212],"performance":[153,215,227],"compared":[154],"no":[156],"technique.":[158],"Spatial":[159],"smoothing":[160,260,281],"ability":[163],"reduce":[165],"effectiveness":[167],"on":[175,241],"average":[176],"able":[177],"defend":[179],"half":[181],"examples.":[185],"Data":[186],"also":[188],"promising":[190],"results,":[191],"reducing":[192],"successfully":[196],"images":[198],"leads":[209],"slightly":[211],"VGG16":[220,270],"Interestingly,":[222],"improves":[225],"We":[232],"conclude":[233],"most":[236],"suitable":[237,263],"dependent":[240],"used.":[244],"For":[245,266],"model,":[248],"our":[249],"indicate":[251,288],"combination":[254,273],"setup.":[265],"models,":[271],"together":[278],"promising.":[284],"Finally,":[285],"applying":[290],"noise":[292],"phase":[296],"not":[298],"workable":[300],"attack.":[305]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
