{"id":"https://openalex.org/W4413239942","doi":"https://doi.org/10.1007/978-3-032-00642-4_16","title":"Towards a Metric to Assess Neural Network Resilience Against Adversarial Samples","display_name":"Towards a Metric to Assess Neural Network Resilience Against Adversarial Samples","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413239942","doi":"https://doi.org/10.1007/978-3-032-00642-4_16"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-032-00642-4_16","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-032-00642-4_16","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-032-00642-4_16.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/978-3-032-00642-4_16.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119317299","display_name":"Johannes Geier","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106192","display_name":"Technische Hochschule Ingolstadt","ror":"https://ror.org/02bxzcy64","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210106192"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Johannes Geier","raw_affiliation_strings":["Technische Hochschule Ingolstadt, Esplanade 10, 85049, Ingolstadt, Germany"],"raw_orcid":"https://orcid.org/0009-0007-1892-4622","affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Esplanade 10, 85049, Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5093383496","display_name":"Patrizia Heinl","orcid":"https://orcid.org/0009-0001-1594-2119"},"institutions":[{"id":"https://openalex.org/I4210106192","display_name":"Technische Hochschule Ingolstadt","ror":"https://ror.org/02bxzcy64","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210106192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Patrizia Heinl","raw_affiliation_strings":["Technische Hochschule Ingolstadt, Esplanade 10, 85049, Ingolstadt, Germany"],"raw_orcid":"https://orcid.org/0009-0001-1594-2119","affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Esplanade 10, 85049, Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5119317299"],"corresponding_institution_ids":["https://openalex.org/I4210106192"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15674523,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"272","last_page":"290"},"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8756703734397888},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7483457922935486},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5483688116073608},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49171024560928345},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4489917457103729},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4422111511230469},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.43149811029434204},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43030303716659546},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0724671483039856}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8756703734397888},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7483457922935486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5483688116073608},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49171024560928345},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4489917457103729},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4422111511230469},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.43149811029434204},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43030303716659546},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0724671483039856},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-032-00642-4_16","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-032-00642-4_16","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-032-00642-4_16.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-032-00642-4_16","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-032-00642-4_16","pdf_url":"https://link.springer.com/content/pdf/10.1007/978-3-032-00642-4_16.pdf","source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[{"score":0.5099999904632568,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413239942.pdf","grobid_xml":"https://content.openalex.org/works/W4413239942.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1963581647","https://openalex.org/W2099779069","https://openalex.org/W2180612164","https://openalex.org/W2243397390","https://openalex.org/W2290944024","https://openalex.org/W2408141691","https://openalex.org/W2561179224","https://openalex.org/W2570685808","https://openalex.org/W2594877703","https://openalex.org/W2603766943","https://openalex.org/W2614505876","https://openalex.org/W2783610119","https://openalex.org/W2896457183","https://openalex.org/W2913848079","https://openalex.org/W2963857521","https://openalex.org/W2963952467","https://openalex.org/W2965189679","https://openalex.org/W2970971581","https://openalex.org/W2997532515","https://openalex.org/W2998771449","https://openalex.org/W3018757597","https://openalex.org/W3091870967","https://openalex.org/W3100471085","https://openalex.org/W3202320739","https://openalex.org/W4206366822","https://openalex.org/W4293580221","https://openalex.org/W4293865127","https://openalex.org/W4295312788","https://openalex.org/W4297814571","https://openalex.org/W4312933868","https://openalex.org/W4385374425"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W4285226279","https://openalex.org/W4288019534"],"abstract_inverted_index":{"Abstract":[0],"Neural":[1],"networks":[2],"are":[3],"vulnerable":[4],"to":[5,101],"adversarial":[6],"attacks.":[7],"Existing":[8],"robustness":[9,17,23,50,87],"evaluation":[10,24,93],"methods":[11],"have":[12],"notable":[13],"limitations,":[14],"which":[15],"makes":[16],"assessment":[18],"challenging.":[19],"This":[20],"work":[21],"explores":[22],"techniques":[25],"and":[26,41,63,73,83,94],"identifies":[27],"key":[28],"factors,":[29,47],"including":[30],"distance":[31],"metrics,":[32],"loss":[33,74],"functions,":[34],"attack":[35],"generation":[36],"algorithms,":[37],"attacker":[38,68],"models,":[39,69],"specificity,":[40],"computational":[42],"resources.":[43],"Building":[44],"on":[45],"those":[46],"a":[48,76,90],"novel":[49],"metric":[51,58,88],"for":[52,60],"classification":[53],"tasks":[54],"is":[55],"proposed.":[56],"Our":[57,86],"accounts":[59],"both,":[61],"targeted":[62],"untargeted":[64],"attacks":[65],"across":[66],"three":[67],"while":[70],"incorporating":[71],"accuracy":[72],"into":[75,97],"weighted":[77],"aggregation.":[78],"The":[79],"scoring":[80],"includes":[81],"robustness-versus-perturbation":[82],"loss-versus-perturbation":[84],"curves.":[85],"offers":[89],"more":[91],"reliable":[92],"deeper":[95],"insights":[96],"model":[98],"vulnerability":[99],"compared":[100],"previous":[102],"approaches.":[103]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}