{"id":"https://openalex.org/W3163966458","doi":"https://doi.org/10.1145/3433210.3453108","title":"DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation","display_name":"DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation","publication_year":2021,"publication_date":"2021-05-24","ids":{"openalex":"https://openalex.org/W3163966458","doi":"https://doi.org/10.1145/3433210.3453108","mag":"3163966458"},"language":"en","primary_location":{"id":"doi:10.1145/3433210.3453108","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3433210.3453108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security","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/A5019692903","display_name":"Han Qiu","orcid":"https://orcid.org/0000-0003-2678-8070"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Han Qiu","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025846619","display_name":"Yi Zeng","orcid":"https://orcid.org/0000-0002-9595-9091"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Zeng","raw_affiliation_strings":["University of California, San Diego, La Jolla, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, San Diego, La Jolla, CA, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073264981","display_name":"Shangwei Guo","orcid":"https://orcid.org/0000-0002-6443-5308"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangwei Guo","raw_affiliation_strings":["Chongqing University, Chongqing, China"],"affiliations":[{"raw_affiliation_string":"Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101591101","display_name":"Tianwei Zhang","orcid":"https://orcid.org/0000-0001-6595-6650"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tianwei Zhang","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083330935","display_name":"Meikang Qiu","orcid":"https://orcid.org/0000-0002-1004-0140"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meikang Qiu","raw_affiliation_strings":["Texas A&amp;M University, TX, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&amp;M University, TX, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072193842","display_name":"Bhavani Thuraisingham","orcid":"https://orcid.org/0000-0003-4653-2080"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhavani Thuraisingham","raw_affiliation_strings":["The University of Texas at Dallas, Dallas, TX, USA"],"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Dallas, TX, USA","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5019692903"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":21.5544,"has_fulltext":false,"cited_by_count":181,"citation_normalized_percentile":{"value":0.9957759,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"363","last_page":"377"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9998000264167786,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9962000250816345,"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/backdoor","display_name":"Backdoor","score":0.9986876249313354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7517009973526001},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.7130237221717834},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.47422361373901367},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24187812209129333}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.9986876249313354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7517009973526001},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.7130237221717834},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.47422361373901367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24187812209129333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3433210.3453108","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3433210.3453108","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7675662412","display_name":null,"funder_award_id":"61832013","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2013593022","https://openalex.org/W2055389753","https://openalex.org/W2067713319","https://openalex.org/W2117130368","https://openalex.org/W2151047074","https://openalex.org/W2194775991","https://openalex.org/W2282821441","https://openalex.org/W2395106899","https://openalex.org/W2536626143","https://openalex.org/W2607219512","https://openalex.org/W2608583225","https://openalex.org/W2735607295","https://openalex.org/W2753783305","https://openalex.org/W2807363941","https://openalex.org/W2889985731","https://openalex.org/W2902543210","https://openalex.org/W2934843808","https://openalex.org/W2954978443","https://openalex.org/W2954996726","https://openalex.org/W2962887844","https://openalex.org/W2962933288","https://openalex.org/W2963268689","https://openalex.org/W2963384482","https://openalex.org/W2964041528","https://openalex.org/W2966689772","https://openalex.org/W2979693078","https://openalex.org/W2979775173","https://openalex.org/W2985913519","https://openalex.org/W2986013765","https://openalex.org/W2990270730","https://openalex.org/W3008901592","https://openalex.org/W3016465224","https://openalex.org/W3020531607","https://openalex.org/W3024103409","https://openalex.org/W3037225663","https://openalex.org/W3089683609","https://openalex.org/W3096264229","https://openalex.org/W3098772125","https://openalex.org/W3099319035","https://openalex.org/W3163083600","https://openalex.org/W4252979261","https://openalex.org/W6819283011"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4320031223","https://openalex.org/W4200629851","https://openalex.org/W4281902577","https://openalex.org/W4309417370","https://openalex.org/W4292107232","https://openalex.org/W3009072493","https://openalex.org/W4401407399"],"abstract_inverted_index":{"Public":[0],"resources":[1],"and":[2,54,67],"services":[3],"(e.g.,":[4],"datasets,":[5],"training":[6],"platforms,":[7],"pre-trained":[8],"models)":[9],"have":[10,61],"been":[11,62],"widely":[12],"adopted":[13],"to":[14,76],"ease":[15],"the":[16,24,35],"development":[17],"of":[18],"Deep":[19],"Learning-based":[20],"applications.":[21,56],"However,":[22],"if":[23],"third-party":[25],"providers":[26],"are":[27,73],"untrusted,":[28],"they":[29],"can":[30,47],"inject":[31],"poisoned":[32],"samples":[33],"into":[34],"datasets":[36],"or":[37],"embed":[38],"backdoors":[39],"in":[40,52,80],"those":[41,78],"models.":[42],"Such":[43],"an":[44],"integrity":[45],"breach":[46],"cause":[48],"severe":[49],"consequences,":[50],"especially":[51],"safety-":[53],"security-critical":[55],"Various":[57],"backdoor":[58],"attack":[59],"techniques":[60],"proposed":[63],"for":[64],"higher":[65],"effectiveness":[66],"stealthiness.":[68],"Unfortunately,":[69],"existing":[70],"defense":[71],"solutions":[72],"not":[74],"practical":[75],"thwart":[77],"attacks":[79],"a":[81],"comprehensive":[82],"way.":[83]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":39},{"year":2023,"cited_by_count":67},{"year":2022,"cited_by_count":38},{"year":2021,"cited_by_count":10}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
