{"id":"https://openalex.org/W3106646114","doi":"https://doi.org/10.1145/3372297.3423362","title":"Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features","display_name":"Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features","publication_year":2020,"publication_date":"2020-10-30","ids":{"openalex":"https://openalex.org/W3106646114","doi":"https://doi.org/10.1145/3372297.3423362","mag":"3106646114"},"language":"en","primary_location":{"id":"doi:10.1145/3372297.3423362","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372297.3423362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101737739","display_name":"Junyu Lin","orcid":"https://orcid.org/0000-0002-5555-3935"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyu Lin","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781025","display_name":"Lei Xu","orcid":"https://orcid.org/0000-0001-6435-6055"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Xu","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080886129","display_name":"Yingqi Liu","orcid":"https://orcid.org/0000-0002-8312-0088"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yingqi Liu","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107249133","display_name":"Xiangyu Zhang","orcid":"https://orcid.org/0000-0002-9544-2500"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiangyu Zhang","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.2697,"has_fulltext":false,"cited_by_count":199,"citation_normalized_percentile":{"value":0.98865987,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"113","last_page":"131"},"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":0.9692000150680542,"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":0.9381999969482422,"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/backdoor","display_name":"Backdoor","score":0.9989588856697083},{"id":"https://openalex.org/keywords/trojan","display_name":"Trojan","score":0.9533460140228271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7592493295669556},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5340836048126221},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4904036521911621},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.4872342646121979},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43110793828964233}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.9989588856697083},{"id":"https://openalex.org/C174333608","wikidata":"https://www.wikidata.org/wiki/Q19635","display_name":"Trojan","level":2,"score":0.9533460140228271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7592493295669556},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5340836048126221},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4904036521911621},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4872342646121979},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43110793828964233}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3372297.3423362","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372297.3423362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1849277567","https://openalex.org/W1861492603","https://openalex.org/W1998808035","https://openalex.org/W2019464758","https://openalex.org/W2096733369","https://openalex.org/W2117539524","https://openalex.org/W2145287260","https://openalex.org/W2153579005","https://openalex.org/W2165698076","https://openalex.org/W2250539671","https://openalex.org/W2325939864","https://openalex.org/W2395579298","https://openalex.org/W2507974895","https://openalex.org/W2512472178","https://openalex.org/W2543927648","https://openalex.org/W2597507805","https://openalex.org/W2603766943","https://openalex.org/W2611576673","https://openalex.org/W2613718673","https://openalex.org/W2753783305","https://openalex.org/W2769506033","https://openalex.org/W2786808285","https://openalex.org/W2807363941","https://openalex.org/W2932026309","https://openalex.org/W2934843808","https://openalex.org/W2962939738","https://openalex.org/W2963037989","https://openalex.org/W2963184668","https://openalex.org/W2963343288","https://openalex.org/W2963749936","https://openalex.org/W2964043980","https://openalex.org/W2964082701","https://openalex.org/W2973217491","https://openalex.org/W2985913519","https://openalex.org/W2986013765","https://openalex.org/W2990270730","https://openalex.org/W3034258347","https://openalex.org/W3099206234","https://openalex.org/W3125713917","https://openalex.org/W4252979261"],"related_works":["https://openalex.org/W4320031223","https://openalex.org/W4200629851","https://openalex.org/W3135176461","https://openalex.org/W3106646114","https://openalex.org/W4308244459","https://openalex.org/W4221166349","https://openalex.org/W4200628936","https://openalex.org/W4387929148","https://openalex.org/W4389518867","https://openalex.org/W4401407399"],"abstract_inverted_index":{"With":[0],"the":[1,48,142,148,178,193,199,203],"prevalent":[2],"use":[3],"of":[4,13,17,76,115,177,205],"Deep":[5],"Neural":[6],"Networks":[7],"(DNNs)":[8],"in":[9,147,190],"many":[10,75],"applications,":[11],"security":[12],"these":[14],"networks":[15],"is":[16,50,145],"importance.":[18],"Pre-trained":[19],"DNNs":[20],"may":[21],"contain":[22],"backdoors":[23,180],"that":[24,83,103,120,157],"are":[25,38,78,195],"injected":[26,179],"through":[27],"poisoned":[28],"training.":[29],"These":[30],"trojaned":[31],"models":[32],"perform":[33],"well":[34],"when":[35,47,141],"regular":[36],"inputs":[37],"provided,":[39],"but":[40],"misclassify":[41],"to":[42,80,132],"a":[43,53,85,96,121,125,161],"target":[44],"output":[45],"label":[46],"input":[49],"stamped":[51],"with":[52,124,168],"unique":[54],"pattern":[55],"called":[56],"trojan":[57,81,101,108],"trigger.":[58,88],"Recently":[59],"various":[60],"backdoor":[61,105,127,171],"detection":[62],"and":[63,99,139,208],"mitigation":[64],"systems":[65],"for":[66],"DNN":[67],"based":[68],"AI":[69],"applications":[70],"have":[71],"been":[72],"proposed.":[73],"However,":[74],"them":[77],"limited":[79],"attacks":[82],"require":[84],"specific":[86],"patch":[87],"In":[89,198],"this":[90,158],"paper,":[91],"we":[92,201],"introduce":[93],"composite":[94,143],"attack,":[95],"more":[97],"flexible":[98],"stealthy":[100],"attack":[102,159,167,207],"eludes":[104],"scanners":[106,194],"using":[107],"triggers":[109],"composed":[110,126],"from":[111],"existing":[112],"benign":[113,137],"features":[114],"multiple":[116],"labels.":[117],"We":[118,164,187],"show":[119,156,175],"neural":[122],"network":[123],"can":[128,181],"achieve":[129],"accuracy":[130],"comparable":[131],"its":[133],"original":[134],"version":[135],"on":[136,152],"data":[138],"misclassifies":[140],"trigger":[144],"present":[146],"input.":[149],"Our":[150],"experiments":[151],"7":[153],"different":[154],"tasks":[155],"poses":[160],"severe":[162],"threat.":[163],"evaluate":[165],"our":[166,206],"two":[169],"state-of-the-art":[170],"scanners.":[172],"The":[173],"results":[174],"none":[176],"be":[182],"detected":[183],"by":[184],"either":[185],"scanner.":[186],"also":[188],"study":[189],"details":[191],"why":[192],"not":[196],"effective.":[197],"end,":[200],"discuss":[202],"essence":[204],"propose":[209],"possible":[210],"defense.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":64},{"year":2023,"cited_by_count":40},{"year":2022,"cited_by_count":32},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
