{"id":"https://openalex.org/W3080270960","doi":"https://doi.org/10.1145/3394486.3403084","title":"Vulnerability vs. Reliability: Disentangled Adversarial Examples for Cross-Modal Learning","display_name":"Vulnerability vs. Reliability: Disentangled Adversarial Examples for Cross-Modal Learning","publication_year":2020,"publication_date":"2020-08-20","ids":{"openalex":"https://openalex.org/W3080270960","doi":"https://doi.org/10.1145/3394486.3403084","mag":"3080270960"},"language":"en","primary_location":{"id":"doi:10.1145/3394486.3403084","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","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/A5100323179","display_name":"Chao Li","orcid":"https://orcid.org/0000-0002-2391-7319"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chao Li","raw_affiliation_strings":["Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079922796","display_name":"Haoteng Tang","orcid":"https://orcid.org/0000-0003-0323-1755"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoteng Tang","raw_affiliation_strings":["University of Pittsburgh, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015874725","display_name":"Cheng Deng","orcid":"https://orcid.org/0000-0003-2620-3247"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Deng","raw_affiliation_strings":["Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001309013","display_name":"Liang Zhan","orcid":"https://orcid.org/0000-0002-7920-4828"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liang Zhan","raw_affiliation_strings":["University of Pittsburgh, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100431792","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0002-3865-8145"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100323179"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":1.5087,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.86418716,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"421","last_page":"429"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9997000098228455,"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":0.9997000098228455,"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/T11307","display_name":"Domain Adaptation and Few-Shot 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9954000115394592,"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/modal","display_name":"Modal","score":0.7829896211624146},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7135770320892334},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7093967199325562},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.685677170753479},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6364908218383789},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6358906030654907},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6180226802825928},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5419401526451111},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.479879766702652},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4664178788661957},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44834017753601074},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.13487693667411804}],"concepts":[{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.7829896211624146},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7135770320892334},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7093967199325562},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.685677170753479},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6364908218383789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6358906030654907},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6180226802825928},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5419401526451111},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.479879766702652},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4664178788661957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44834017753601074},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.13487693667411804},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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:10.1145/3394486.3403084","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394486.3403084","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1910300841","https://openalex.org/W1922199343","https://openalex.org/W1932198206","https://openalex.org/W1933349210","https://openalex.org/W1992371516","https://openalex.org/W2007972815","https://openalex.org/W2099471712","https://openalex.org/W2109586012","https://openalex.org/W2138118304","https://openalex.org/W2151368806","https://openalex.org/W2155803963","https://openalex.org/W2179488730","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2251864938","https://openalex.org/W2266728343","https://openalex.org/W2388114291","https://openalex.org/W2402144811","https://openalex.org/W2460937040","https://openalex.org/W2483935403","https://openalex.org/W2543927648","https://openalex.org/W2604880013","https://openalex.org/W2621147812","https://openalex.org/W2765440071","https://openalex.org/W2774644650","https://openalex.org/W2787487383","https://openalex.org/W2791083848","https://openalex.org/W2795832645","https://openalex.org/W2799244840","https://openalex.org/W2894874917","https://openalex.org/W2895249671","https://openalex.org/W2903903916","https://openalex.org/W2922521335","https://openalex.org/W2949999304","https://openalex.org/W2953276893","https://openalex.org/W2954182683","https://openalex.org/W2962706528","https://openalex.org/W2962835968","https://openalex.org/W2962955826","https://openalex.org/W2963187862","https://openalex.org/W2963207607","https://openalex.org/W2963857521","https://openalex.org/W2963966654","https://openalex.org/W2964153729","https://openalex.org/W2964158883","https://openalex.org/W2968980846","https://openalex.org/W2970652125","https://openalex.org/W2970966260","https://openalex.org/W2972421866","https://openalex.org/W2981138049","https://openalex.org/W2982905682","https://openalex.org/W2999606372","https://openalex.org/W3035569111","https://openalex.org/W3103557498","https://openalex.org/W6600529467"],"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/W627697492"],"abstract_inverted_index":{"The":[0],"vulnerability":[1],"of":[2,11,41,52,101,115,147,198],"deep":[3,42,173],"neural":[4],"networks":[5],"has":[6,30],"gained":[7],"a":[8,24,28,171],"great":[9],"upsurge":[10],"research":[12],"attention,":[13],"which":[14],"engages":[15],"well-designed":[16],"examples":[17,36,54,73,143,163,191],"through":[18,128],"adding":[19],"little":[20],"perturbations":[21,117],"to":[22,37,97,118,140],"fool":[23],"well-performed":[25],"network.":[26,176],"Meanwhile,":[27],"progress":[29],"been":[31],"made":[32],"in":[33,59,192],"leveraging":[34,187],"adversarial":[35,53,116,162],"boost":[38],"the":[39,50,99,104,113,123,131,135,145,161,178,196],"robustness":[40,197],"cross-modal":[43,47,61,83,120,148,157,174],"networks.":[44],"However,":[45],"for":[46,74],"learning,":[48,76],"both":[49],"causes":[51],"and":[55,91,94],"their":[56],"latent":[57],"advantages":[58],"learning":[60],"correlations":[62],"are":[63,167],"under-explored.":[64],"In":[65],"this":[66,109,182,199],"paper,":[67],"we":[68,80,111],"propose":[69],"novel":[70],"Disentangled":[71],"Adversarial":[72],"Cross-Modal":[75],"dubbed":[77],"DACM.":[78],"Specifically,":[79],"first":[81],"divide":[82],"data":[84],"into":[85],"two":[86,156],"aspects,":[87],"namely":[88],"modality-related":[89,105,124,142,190],"component":[90,125],"modality-unrelated":[92,132],"counterpart,":[93],"then":[95],"learn":[96],"improve":[98],"reliability":[100],"network":[102],"using":[103],"component.":[106,133],"To":[107],"achieve":[108],"goal,":[110],"apply":[112],"generation":[114],"strengthen":[119],"correlations,":[121],"wherein":[122],"is":[126,138],"acquired":[127],"gradually":[129],"detaching":[130],"Finally,":[134],"proposed":[136],"DACM":[137,166],"employed":[139],"create":[141],"towards":[144],"application":[146],"hashing":[149,175],"retrieval.":[150],"Extensive":[151],"experiments":[152],"carried":[153],"out":[154],"on":[155],"benchmarks":[158],"show":[159],"that":[160],"learned":[164],"by":[165,185],"efficient":[168],"at":[169],"fooling":[170],"target":[172,183],"On":[177],"other":[179],"hand,":[180],"training":[181],"model":[184,200],"merely":[186],"our":[188],"created":[189],"turn":[193],"significantly":[194],"promotes":[195],"itself.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}