{"id":"https://openalex.org/W4404613319","doi":"https://doi.org/10.1145/3689942.3694746","title":"The Security of Deep Learning Defenses in Medical Imaging","display_name":"The Security of Deep Learning Defenses in Medical Imaging","publication_year":2023,"publication_date":"2023-11-20","ids":{"openalex":"https://openalex.org/W4404613319","doi":"https://doi.org/10.1145/3689942.3694746"},"language":"en","primary_location":{"id":"doi:10.1145/3689942.3694746","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3689942.3694746","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 Workshop on Cybersecurity in Healthcare","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3689942.3694746","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054101827","display_name":"Moshe Levy","orcid":"https://orcid.org/0000-0001-5061-4404"},"institutions":[{"id":"https://openalex.org/I13955877","display_name":"Bar-Ilan University","ror":"https://ror.org/03kgsv495","country_code":"IL","type":"education","lineage":["https://openalex.org/I13955877"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Moshe Levy","raw_affiliation_strings":["Bar-Ilan University, Ramat-gan, Israel"],"raw_orcid":"https://orcid.org/0000-0001-5061-4404","affiliations":[{"raw_affiliation_string":"Bar-Ilan University, Ramat-gan, Israel","institution_ids":["https://openalex.org/I13955877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084959917","display_name":"Guy Amit","orcid":"https://orcid.org/0000-0002-5987-4402"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Guy Amit","raw_affiliation_strings":["Ben-Gurion University, Beersheva, Israel"],"raw_orcid":"https://orcid.org/0000-0002-5987-4402","affiliations":[{"raw_affiliation_string":"Ben-Gurion University, Beersheva, Israel","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072913672","display_name":"Yuval Elovici","orcid":"https://orcid.org/0000-0002-9641-128X"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Yuval Elovici","raw_affiliation_strings":["Ben Gurion University, Beersheva, Israel"],"raw_orcid":"https://orcid.org/0000-0002-9641-128X","affiliations":[{"raw_affiliation_string":"Ben Gurion University, Beersheva, Israel","institution_ids":["https://openalex.org/I124227911"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047995605","display_name":"Yisroel Mirsky","orcid":"https://orcid.org/0000-0001-6367-2734"},"institutions":[{"id":"https://openalex.org/I124227911","display_name":"Ben-Gurion University of the Negev","ror":"https://ror.org/05tkyf982","country_code":"IL","type":"education","lineage":["https://openalex.org/I124227911"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Yisroel Mirsky","raw_affiliation_strings":["Ben-Gurion University, Beersheva, Israel"],"raw_orcid":"https://orcid.org/0000-0001-6367-2734","affiliations":[{"raw_affiliation_string":"Ben-Gurion University, Beersheva, Israel","institution_ids":["https://openalex.org/I124227911"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"37","last_page":"44"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9993000030517578,"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":0.9993000030517578,"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9631999731063843,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.612826406955719},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4866217076778412},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.48380059003829956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42566561698913574},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3770526647567749}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.612826406955719},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4866217076778412},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.48380059003829956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42566561698913574},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3770526647567749}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3689942.3694746","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3689942.3694746","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 Workshop on Cybersecurity in Healthcare","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3689942.3694746","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3689942.3694746","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 Workshop on Cybersecurity in Healthcare","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2543927648","https://openalex.org/W2788633781","https://openalex.org/W2802896687","https://openalex.org/W2924551358","https://openalex.org/W2951934944","https://openalex.org/W2963564844","https://openalex.org/W2963946669","https://openalex.org/W2964082701","https://openalex.org/W2964086320","https://openalex.org/W2964335429","https://openalex.org/W2980214238","https://openalex.org/W3021182036","https://openalex.org/W3027789868","https://openalex.org/W3028178168","https://openalex.org/W3093540382","https://openalex.org/W3164873617","https://openalex.org/W3200113267","https://openalex.org/W3202320739","https://openalex.org/W4289038676","https://openalex.org/W4293865127","https://openalex.org/W4367368092"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"Deep":[0],"learning":[1,112],"has":[2,54],"shown":[3],"great":[4],"promise":[5],"in":[6],"the":[7,49,65,77,86,89,109,116,134],"medical":[8],"image":[9],"analysis":[10],"domain.":[11],"Medical":[12],"professionals":[13],"and":[14,25,114,137],"healthcare":[15,127],"providers":[16],"have":[17],"begun":[18],"to":[19,23,39,88],"adopt":[20],"this":[21,93],"technology":[22],"accelerate":[24],"enhance":[26],"their":[27],"work.":[28],"These":[29],"systems":[30],"use":[31],"deep":[32,111],"neural":[33],"networks":[34],"(DNNs)":[35],"which":[36],"are":[37],"vulnerable":[38],"adversarial":[40,66],"samples:":[41],"images":[42],"with":[43],"imperceivable":[44],"changes":[45],"that":[46,98],"can":[47,70,102],"alter":[48],"model's":[50],"prediction.":[51],"Prior":[52],"research":[53],"proposed":[55],"defenses":[56],"aimed":[57],"at":[58],"making":[59],"DNNs":[60,128],"more":[61],"robust":[62],"or":[63],"detecting":[64],"samples":[67],"before":[68],"they":[69],"do":[71],"any":[72],"harm.":[73],"However,":[74],"none":[75],"of":[76,84,125],"studies":[78],"considered":[79],"an":[80,99],"informed":[81,100],"attacker":[82,101],"capable":[83],"adapting":[85],"attack":[87],"defense":[90,117],"mechanism.":[91],"In":[92],"qualitative":[94],"study,":[95],"we":[96],"show":[97],"evade":[103],"five":[104],"advanced":[105],"defenses,":[106],"successfully":[107],"fooling":[108],"victim":[110],"model":[113],"rendering":[115],"useless.":[118],"We":[119],"also":[120],"propose":[121],"two":[122],"alternative":[123],"means":[124],"securing":[126],"from":[129],"such":[130],"attacks:":[131],"(1)":[132],"hardening":[133],"system's":[135],"security,":[136],"(2)":[138],"using":[139],"digital":[140],"signatures.":[141]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}