{"id":"https://openalex.org/W4410399029","doi":"https://doi.org/10.32473/flairs.38.1.139042","title":"Breaking Machine Learning Models with Adversarial Attacks and its Variants","display_name":"Breaking Machine Learning Models with Adversarial Attacks and its Variants","publication_year":2025,"publication_date":"2025-05-14","ids":{"openalex":"https://openalex.org/W4410399029","doi":"https://doi.org/10.32473/flairs.38.1.139042"},"language":"en","primary_location":{"id":"doi:10.32473/flairs.38.1.139042","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.139042","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/139042/144075","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journals.flvc.org/FLAIRS/article/download/139042/144075","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078441495","display_name":"Pavan Reddy","orcid":"https://orcid.org/0000-0001-7264-8429"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pavan Reddy","raw_affiliation_strings":["The George Washington University, Washington DC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The George Washington University, Washington DC","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5078441495"],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":0.7124,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.70243008,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"38","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9984999895095825,"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":0.9984999895095825,"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":0.9887999892234802,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9650999903678894,"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/adversarial-system","display_name":"Adversarial system","score":0.7824406623840332},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6050506234169006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5693913698196411},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5094533562660217}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7824406623840332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6050506234169006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5693913698196411},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5094533562660217}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.32473/flairs.38.1.139042","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.139042","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/139042/144075","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e7ec532f92b9418892853dd4fb45e13f","is_oa":true,"landing_page_url":"https://doaj.org/article/e7ec532f92b9418892853dd4fb45e13f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the International Florida Artificial Intelligence Research Society Conference, Vol 38, Iss 1 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.32473/flairs.38.1.139042","is_oa":true,"landing_page_url":"https://doi.org/10.32473/flairs.38.1.139042","pdf_url":"https://journals.flvc.org/FLAIRS/article/download/139042/144075","source":{"id":"https://openalex.org/S4210205383","display_name":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","issn_l":"2334-0754","issn":["2334-0754","2334-0762"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":"https://openalex.org/P4310320363","host_organization_name":"George A. Smathers Libraries","host_organization_lineage":["https://openalex.org/P4310320363"],"host_organization_lineage_names":["George A. Smathers Libraries"],"type":"conference"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The International FLAIRS Conference Proceedings","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410399029.pdf","grobid_xml":"https://content.openalex.org/works/W4410399029.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W1945616565","https://openalex.org/W2243397390","https://openalex.org/W2603766943","https://openalex.org/W2640329709","https://openalex.org/W6637162671","https://openalex.org/W6785841629","https://openalex.org/W6966866225"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Machine":[0],"learning":[1,106,125],"models":[2,33],"can":[3],"be":[4],"by":[5],"adversarial":[6,24,81,104,116],"attacks,":[7],"subtle,":[8],"imperceptible":[9],"perturbations":[10],"to":[11,17,79,102],"inputs":[12],"that":[13],"cause":[14],"the":[15,72,94,98],"model":[16,85],"produce":[18],"erroneous":[19],"outputs.":[20],"This":[21],"tutorial":[22],"introduces":[23],"examples":[25,82],"and":[26,36,57,62,71,83,97,118],"its":[27],"variants,":[28],"explaining":[29],"why":[30],"even":[31],"stateof-the-art":[32],"are":[34],"vulnerable":[35],"how":[37],"this":[38],"impacts":[39],"security":[40],"in":[41,87,107],"AI.":[42],"It":[43],"provides":[44],"an":[45],"overview":[46],"of":[47],"key":[48],"concepts":[49],"(such":[50],"as":[51],"black-box":[52],"vs.":[53],"white-box":[54],"attack":[55,60],"scenarios)":[56],"survey":[58],"common":[59],"techniques":[61],"defensive":[63],"strategies.":[64],"A":[65],"hands-on":[66],"component":[67],"using":[68],"Google":[69],"Colab":[70],"open-source":[73],"Adversarial":[74],"Lab":[75],"toolkit":[76],"allows":[77],"attendees":[78],"craft":[80],"test":[84],"robustness":[86],"real":[88],"time.":[89],"Throughout,":[90],"we":[91],"emphasize":[92],"both":[93],"practical":[95],"skills":[96],"ethical":[99],"considerations":[100],"needed":[101],"apply":[103],"machine":[105,124],"a":[108,113],"responsiblemanner.":[109],"Attendees":[110],"will":[111],"gain":[112],"comprehensive":[114],"foundationin":[115],"attacks":[117],"insights":[119],"into":[120],"building":[121],"morerobust,":[122],"secure":[123],"models.":[126]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
