{"id":"https://openalex.org/W3189813398","doi":"https://doi.org/10.24963/ijcai.2021/80","title":"Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks","display_name":"Characteristic Examples: High-Robustness, Low-Transferability Fingerprinting of Neural Networks","publication_year":2021,"publication_date":"2021-08-01","ids":{"openalex":"https://openalex.org/W3189813398","doi":"https://doi.org/10.24963/ijcai.2021/80","mag":"3189813398"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2021/80","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/80","pdf_url":"https://www.ijcai.org/proceedings/2021/0080.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2021/0080.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035205195","display_name":"Siyue Wang","orcid":"https://orcid.org/0009-0005-5705-942X"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Siyue Wang","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100411532","display_name":"Xiao Wang","orcid":"https://orcid.org/0000-0003-4435-7098"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Wang","raw_affiliation_strings":["Boston University"],"affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050344371","display_name":"Pin\u2010Yu Chen","orcid":"https://orcid.org/0000-0003-1039-8369"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pin-Yu Chen","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073885088","display_name":"Pu Zhao","orcid":"https://orcid.org/0000-0001-5018-2859"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Pu Zhao","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043582832","display_name":"Xue Lin","orcid":"https://orcid.org/0000-0001-6210-8883"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Xue Lin","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5035205195"],"corresponding_institution_ids":["https://openalex.org/I87182695"],"apc_list":null,"apc_paid":null,"fwci":1.8195,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.87878089,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"575","last_page":"582"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"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":0.9990000128746033,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.836905300617218},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.8125374913215637},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6858994960784912},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45511749386787415},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.4539657533168793},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4403117895126343},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.438618928194046},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4168594777584076},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36733126640319824},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.07660141587257385}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.836905300617218},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.8125374913215637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6858994960784912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45511749386787415},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.4539657533168793},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4403117895126343},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.438618928194046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4168594777584076},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36733126640319824},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.07660141587257385},{"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},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2021/80","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/80","pdf_url":"https://www.ijcai.org/proceedings/2021/0080.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2021/80","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2021/80","pdf_url":"https://www.ijcai.org/proceedings/2021/0080.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.41999998688697815,"display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G6950086213","display_name":"SaTC: CORE: Medium: Protecting Confidentiality and Integrity of Deep Neural Networks against Side-Channel and Fault Attacks","funder_award_id":"1929300","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3189813398.pdf","grobid_xml":"https://content.openalex.org/works/W3189813398.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1686810756","https://openalex.org/W1916685473","https://openalex.org/W1945616565","https://openalex.org/W2040903332","https://openalex.org/W2108598243","https://openalex.org/W2110096996","https://openalex.org/W2112796928","https://openalex.org/W2140196014","https://openalex.org/W2194775991","https://openalex.org/W2579318729","https://openalex.org/W2748789698","https://openalex.org/W2768064608","https://openalex.org/W2787708942","https://openalex.org/W2891719429","https://openalex.org/W2893359234","https://openalex.org/W2899585792","https://openalex.org/W2919386767","https://openalex.org/W2948833786","https://openalex.org/W2952104986","https://openalex.org/W2962710014","https://openalex.org/W2962847335","https://openalex.org/W2963143631","https://openalex.org/W2963207607","https://openalex.org/W2963389226","https://openalex.org/W2963446712","https://openalex.org/W2963674932","https://openalex.org/W2963771448","https://openalex.org/W2964128659","https://openalex.org/W2964153729","https://openalex.org/W2964253222","https://openalex.org/W2965496811","https://openalex.org/W2966658324","https://openalex.org/W2971126145","https://openalex.org/W2973539365","https://openalex.org/W3030163527","https://openalex.org/W3037475485","https://openalex.org/W3117037102","https://openalex.org/W3118608800","https://openalex.org/W4288000237","https://openalex.org/W4292779060","https://openalex.org/W4293846201","https://openalex.org/W4294506858"],"related_works":["https://openalex.org/W4399895933","https://openalex.org/W2161221533","https://openalex.org/W4229699405","https://openalex.org/W1666484574","https://openalex.org/W2216382288","https://openalex.org/W2355491300","https://openalex.org/W4234629551","https://openalex.org/W2011110943","https://openalex.org/W2028856635","https://openalex.org/W4251615416"],"abstract_inverted_index":{"This":[0,27],"paper":[1],"proposes":[2],"Characteristic":[3],"Examples":[4],"for":[5,39,139],"effectively":[6],"fingerprinting":[7,135],"deep":[8],"neural":[9],"networks,":[10],"featuring":[11],"high-robustness":[12],"to":[13,24,76,114],"the":[14,29,80,87,102,115,123,151],"base":[15,82],"model":[16,18],"against":[17],"pruning":[19],"as":[20,22,111],"well":[21],"low-transferability":[23],"unassociated":[25],"models.":[26],"is":[28],"first":[30],"work":[31],"taking":[32],"both":[33],"robustness":[34,61,90,105],"and":[35,49,62,74,91,106,154],"transferability":[36],"into":[37],"consideration":[38],"generating":[40],"realistic":[41],"fingerprints,":[42],"whereas":[43],"current":[44],"methods":[45],"lack":[46],"practical":[47],"assumptions":[48],"may":[50],"incur":[51,157],"large":[52],"false":[53,116,159],"positive":[54],"rates.":[55],"To":[56,84],"achieve":[57,128],"better":[58],"trade-off":[59,88],"between":[60,89,104],"transferability,":[63,92,107],"we":[64,93],"propose":[65,94],"three":[66],"kinds":[67],"of":[68],"characteristic":[69,125],"examples:":[70],"vanilla":[71],"C-examples,":[72],"RC-examples,":[73],"LTRC-example,":[75],"derive":[77],"fingerprints":[78],"from":[79],"original":[81],"model.":[83],"fairly":[85],"characterize":[86],"Uniqueness":[95],"Score,":[96],"a":[97],"comprehensive":[98],"metric":[99],"that":[100,122],"measures":[101],"difference":[103],"which":[108],"also":[109],"serves":[110],"an":[112],"indicator":[113],"alarm":[117],"problem.":[118],"Extensive":[119],"experiments":[120],"demonstrate":[121],"proposed":[124],"examples":[126],"can":[127],"superior":[129],"performance":[130],"when":[131],"compared":[132],"with":[133],"existing":[134],"methods.":[136],"In":[137],"particular,":[138],"VGG":[140],"ImageNet":[141],"models,":[142],"using":[143],"LTRC-examples":[144],"gives":[145],"4X":[146],"higher":[147],"uniqueness":[148],"score":[149],"than":[150],"baseline":[152],"method":[153],"does":[155],"not":[156],"any":[158],"positives.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}