{"id":"https://openalex.org/W2605631833","doi":"https://doi.org/10.1145/3593078.3593935","title":"Adversarial and Clean Data Are Not Twins","display_name":"Adversarial and Clean Data Are Not Twins","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W2605631833","doi":"https://doi.org/10.1145/3593078.3593935","mag":"2605631833"},"language":"en","primary_location":{"id":"doi:10.1145/3593078.3593935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3593078.3593935","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3593078.3593935","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3593078.3593935","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015667307","display_name":"Zhitao Gong","orcid":"https://orcid.org/0000-0003-1857-4697"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhitao Gong","raw_affiliation_strings":["Auburn University, Auburn, AL, USA"],"raw_orcid":"https://orcid.org/0000-0003-1857-4697","affiliations":[{"raw_affiliation_string":"Auburn University, Auburn, AL, USA","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101402672","display_name":"Wenlu Wang","orcid":"https://orcid.org/0000-0002-4829-1068"},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenlu Wang","raw_affiliation_strings":["Texas A&amp;M University - Corpus Christi, Corpus Christi, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-4829-1068","affiliations":[{"raw_affiliation_string":"Texas A&amp;M University - Corpus Christi, Corpus Christi, TX, USA","institution_ids":["https://openalex.org/I96749437"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.037,"has_fulltext":true,"cited_by_count":190,"citation_normalized_percentile":{"value":0.97029487,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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":1.0,"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":0.9966999888420105,"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":0.9933000206947327,"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/adversarial-system","display_name":"Adversarial system","score":0.9235777258872986},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7483921647071838},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7400041222572327},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6707550883293152},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.6559492349624634},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5761179327964783},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5350645184516907},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4597288966178894},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40173959732055664},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39702779054641724},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35938501358032227},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.14064890146255493},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1023712158203125}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9235777258872986},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7483921647071838},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7400041222572327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6707550883293152},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.6559492349624634},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5761179327964783},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5350645184516907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4597288966178894},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40173959732055664},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39702779054641724},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35938501358032227},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.14064890146255493},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1023712158203125},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3593078.3593935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3593078.3593935","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3593078.3593935","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3593078.3593935","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3593078.3593935","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3593078.3593935","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4399999976158142,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2605631833.pdf","grobid_xml":"https://content.openalex.org/works/W2605631833.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1932198206","https://openalex.org/W1945616565","https://openalex.org/W2108069432","https://openalex.org/W2460937040","https://openalex.org/W2581082771","https://openalex.org/W2971168382","https://openalex.org/W3031922605","https://openalex.org/W3100925007","https://openalex.org/W4256044039"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W3093978547","https://openalex.org/W3203790781","https://openalex.org/W2997056298","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W3127875750","https://openalex.org/W4383221314","https://openalex.org/W2953536436"],"abstract_inverted_index":{"Adversarial":[0,39],"attack":[1],"has":[2],"cast":[3],"a":[4,85,109,180,187],"shadow":[5],"on":[6,137],"the":[7,20,23,32,42,89,93,103,125,132,143,153,156],"massive":[8],"success":[9],"of":[10,155,159],"deep":[11,28],"neural":[12,29],"networks.":[13],"Despite":[14],"being":[15],"almost":[16],"visually":[17],"identical":[18],"to":[19,59,68,108,119,123,190],"clean":[21,94,169],"data,":[22],"adversarial":[24,63,90,111,121,160,167,194],"images":[25],"can":[26,83,176,184],"fool":[27],"networks":[30],"into":[31],"wrong":[33],"predictions":[34],"with":[35,96,179],"very":[36],"high":[37,97],"confidence.":[38],"training,":[40],"as":[41,186],"most":[43],"prevailing":[44],"defense":[45],"technique,":[46],"suffers":[47],"from":[48,92],"class-wise":[49],"unfairness":[50],"and":[51,61,74,168,192],"model-dependent":[52],"challenges.":[53],"In":[54,113],"this":[55,151],"paper,":[56],"we":[57,82,129,148],"propose":[58],"detect":[60,191],"eliminate":[62,193],"data":[64,69,95,170,195,199],"in":[65,71,196],"databases":[66],"prior":[67],"processing":[70],"supporting":[72],"robust":[73,107],"secure":[75],"AI":[76],"workloads.":[77],"We":[78,99],"empirically":[79,130],"show":[80,101],"that":[81,102,150,166,175],"build":[84],"binary":[86,104,126,144,181],"classifier":[87,105,145],"separating":[88],"apart":[91],"accuracy.":[98],"also":[100],"is":[106,117,152],"second-round":[110],"attack.":[112],"other":[114],"words,":[115],"it":[116],"difficult":[118],"disguise":[120],"samples":[122],"bypass":[124],"classifier.":[127],"Furthermore,":[128],"investigate":[131],"generalization":[133],"limitation":[134],"which":[135,183],"lingers":[136],"all":[138],"current":[139],"defensive":[140],"methods,":[141],"including":[142],"approach.":[146],"And":[147],"hypothesize":[149],"result":[154],"intrinsic":[157],"property":[158],"crafting":[161],"algorithms.":[162],"Our":[163],"experiments":[164],"ascertain":[165],"are":[171],"two":[172],"different":[173],"datasets":[174],"be":[177],"separated":[178],"classifier,":[182],"serve":[185],"portable":[188],"component":[189],"an":[197],"end-to-end":[198],"management":[200],"pipeline.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":38},{"year":2020,"cited_by_count":43},{"year":2019,"cited_by_count":32},{"year":2018,"cited_by_count":23},{"year":2017,"cited_by_count":12}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
