{"id":"https://openalex.org/W4212811856","doi":"https://doi.org/10.1117/12.2611199","title":"Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases","display_name":"Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4212811856","doi":"https://doi.org/10.1117/12.2611199"},"language":"en","primary_location":{"id":"doi:10.1117/12.2611199","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2611199","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"Medical Imaging 2022: Computer-Aided Diagnosis","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Computer-Aided Diagnosis","raw_type":"proceedings-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/A5073656529","display_name":"Erikson J. de Aguiar","orcid":"https://orcid.org/0000-0001-8563-8051"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Erikson J\u00falio de Aguiar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037665595","display_name":"Karem D. Marcomini","orcid":"https://orcid.org/0000-0002-8594-4654"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karem Daiane Marcomini","raw_affiliation_strings":["Univ. de S\u00e3o Paulo (Brazil)"],"affiliations":[{"raw_affiliation_string":"Univ. de S\u00e3o Paulo (Brazil)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025090888","display_name":"Felipe Antunes Quirino","orcid":"https://orcid.org/0000-0002-4439-9450"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Felipe Antunes Quirino","raw_affiliation_strings":["Univ. de S\u00e3o Paulo (Brazil)"],"affiliations":[{"raw_affiliation_string":"Univ. de S\u00e3o Paulo (Brazil)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085369510","display_name":"Marco A. Guti\u00e9rrez","orcid":"https://orcid.org/0000-0003-0964-6222"},"institutions":[{"id":"https://openalex.org/I4210140911","display_name":"Hospital do Cora\u00e7\u00e3o","ror":"https://ror.org/04dzaw261","country_code":"BR","type":"healthcare","lineage":["https://openalex.org/I4210140911"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marco A. Gutierrez","raw_affiliation_strings":["Instituto do Cora\u00e7\u00e3o do Hospital das Cl\u00ednicas (Brazil)"],"affiliations":[{"raw_affiliation_string":"Instituto do Cora\u00e7\u00e3o do Hospital das Cl\u00ednicas (Brazil)","institution_ids":["https://openalex.org/I4210140911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020611078","display_name":"Caetano Traina","orcid":"https://orcid.org/0000-0002-6625-6047"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Caetano Traina","raw_affiliation_strings":["Univ. de S\u00e3o Paulo (Brazil)"],"affiliations":[{"raw_affiliation_string":"Univ. de S\u00e3o Paulo (Brazil)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014783020","display_name":"Agma J. M. Traina","orcid":"https://orcid.org/0000-0003-4929-7258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agma J. M. Traina","raw_affiliation_strings":["Univ. de S\u00e3o Paulo (Brazil)"],"affiliations":[{"raw_affiliation_string":"Univ. de S\u00e3o Paulo (Brazil)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5073656529"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4162,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.54705597,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"7","issue":null,"first_page":"122","last_page":"122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987000226974487,"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":0.9987000226974487,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9980000257492065,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8656882047653198},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.7222979068756104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.698563814163208},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5375333428382874},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5049198269844055},{"id":"https://openalex.org/keywords/2019-20-coronavirus-outbreak","display_name":"2019-20 coronavirus outbreak","score":0.41144293546676636},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3411993384361267},{"id":"https://openalex.org/keywords/virology","display_name":"Virology","score":0.13177117705345154},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09781676530838013}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8656882047653198},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.7222979068756104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.698563814163208},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5375333428382874},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5049198269844055},{"id":"https://openalex.org/C3006700255","wikidata":"https://www.wikidata.org/wiki/Q81068910","display_name":"2019-20 coronavirus outbreak","level":3,"score":0.41144293546676636},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3411993384361267},{"id":"https://openalex.org/C159047783","wikidata":"https://www.wikidata.org/wiki/Q7215","display_name":"Virology","level":1,"score":0.13177117705345154},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09781676530838013},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C116675565","wikidata":"https://www.wikidata.org/wiki/Q3241045","display_name":"Outbreak","level":2,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2611199","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2611199","pdf_url":null,"source":{"id":"https://openalex.org/S4363606689","display_name":"Medical Imaging 2022: Computer-Aided Diagnosis","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1565746575","https://openalex.org/W1945616565","https://openalex.org/W2059700429","https://openalex.org/W2519380942","https://openalex.org/W2955127209","https://openalex.org/W2979479162","https://openalex.org/W3047095708","https://openalex.org/W3091787640","https://openalex.org/W3135057764","https://openalex.org/W3164873617","https://openalex.org/W4250685322","https://openalex.org/W4293846201","https://openalex.org/W4353004773","https://openalex.org/W6640425456","https://openalex.org/W6674592523","https://openalex.org/W6726114608","https://openalex.org/W6739868092","https://openalex.org/W6766629975"],"related_works":["https://openalex.org/W3036314732","https://openalex.org/W3009669391","https://openalex.org/W3154141118","https://openalex.org/W4394620624","https://openalex.org/W4296163649","https://openalex.org/W4388896133","https://openalex.org/W3031607536","https://openalex.org/W3135208316","https://openalex.org/W4400613637","https://openalex.org/W3107152225"],"abstract_inverted_index":{"The":[0,36,161,237],"SARS-CoV-2":[1],"(COVID-19)":[2],"disease":[3],"rapidly":[4],"spread":[5],"worldwide,":[6],"thus":[7],"increasing":[8],"the":[9,43,100,106,124,137,151,154,174,185,198,212,218,222,243,251],"need":[10],"to":[11,15,25,28,58,65,76,82,90,133,136,209,216,234],"create":[12],"new":[13,88],"strategies":[14],"fight":[16],"it.":[17],"Several":[18],"researchers":[19],"in":[20,52,246,250],"different":[21],"fields":[22],"have":[23,73],"attempted":[24],"develop":[26],"methods":[27,89],"early":[29],"identifying":[30],"it":[31,183],"and":[32,61,158,171,197],"mitigating":[33],"its":[34,205],"effects.":[35],"Deep":[37],"Learning":[38],"(DL)":[39],"approach,":[40],"such":[41],"as":[42],"Convolutional":[44],"Neural":[45],"Networks":[46],"(CNNs),":[47],"has":[48],"been":[49],"increasingly":[50],"used":[51],"COVID-19":[53,119,231],"diagnoses.":[54],"These":[55,85],"models":[56,72,93,113,164,192,229],"intend":[57],"support":[59,77],"decision-making":[60],"are":[62,80,87,232],"doing":[63],"well":[64],"detecting":[66],"patient":[67],"status":[68],"early.":[69],"Although":[70],"DL":[71,92,112,228,252],"good":[74],"accuracy":[75],"diagnosis,":[78],"they":[79],"vulnerable":[81,233],"Adversarial":[83,109,235],"Attacks.":[84],"attacks":[86],"make":[91],"biased":[94],"by":[95,140,221],"adding":[96,159],"small":[97],"perturbations":[98,135],"on":[99,111,123],"original":[101],"image.":[102,148],"This":[103],"paper":[104],"investigates":[105],"impact":[107],"of":[108,118,241],"Attacks":[110],"for":[114,230],"classifying":[115],"X-ray":[116],"images":[117,139],"cases.":[120],"We":[121,149],"focused":[122],"attack":[125],"Fast":[126],"Gradient":[127],"Sign":[128],"Method":[129],"(FGSM),":[130],"which":[131],"aims":[132],"add":[134],"testing":[138],"combining":[141],"a":[142,146,247],"perturbation":[143],"matrix,":[144],"producing":[145],"crafted":[147],"conduct":[150],"experiments":[152],"analyzing":[153],"model\u2019s":[155],"performance":[156,195],"attack-free":[157,175],"attacks.":[160,223],"following":[162],"CNNs":[163],"were":[165],"selected:":[166],"DenseNet201,":[167],"ResNet-50V2,":[168],"MobileNetV2,":[169],"NasNet":[170],"VGG16.":[172],"In":[173],"environment,":[176],"we":[177],"reach":[178],"precision":[179],"around":[180],"99%.":[181],"When":[182],"adds":[184],"attack,":[186],"our":[187],"results":[188],"revealed":[189],"that":[190,203,227],"all":[191],"suffer":[193],"from":[194,207],"reduction,":[196],"most":[199],"affected":[200,220],"was":[201,239],"MobileNet":[202],"reduced":[204],"ability":[206],"98.61%":[208],"67.73%.":[210],"However,":[211],"VGG16":[213],"network":[214],"showed":[215],"be":[217],"least":[219],"Our":[224],"finds":[225],"describe":[226],"Examples.":[236],"FGSM":[238],"capable":[240],"fooling":[242],"model,":[244],"resulting":[245],"significant":[248],"reduction":[249],"performance.":[253]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}