{"id":"https://openalex.org/W4402979050","doi":"https://doi.org/10.1109/icme57554.2024.10688012","title":"Time-Frequency Jointed Imperceptible Adversarial Attack to Brainprint Recognition with Deep Learning Models","display_name":"Time-Frequency Jointed Imperceptible Adversarial Attack to Brainprint Recognition with Deep Learning Models","publication_year":2024,"publication_date":"2024-07-15","ids":{"openalex":"https://openalex.org/W4402979050","doi":"https://doi.org/10.1109/icme57554.2024.10688012"},"language":"en","primary_location":{"id":"doi:10.1109/icme57554.2024.10688012","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme57554.2024.10688012","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Multimedia and Expo (ICME)","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/A5109636603","display_name":"Hangjie Yi","orcid":"https://orcid.org/0009-0008-1513-3793"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hangjie Yi","raw_affiliation_strings":["Hangzhou Dianzi University,School of Computer Science,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,School of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067392418","display_name":"Yuhang Ming","orcid":"https://orcid.org/0000-0002-4548-6388"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhang Ming","raw_affiliation_strings":["Hangzhou Dianzi University,School of Computer Science,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,School of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101968314","display_name":"Dongjun Liu","orcid":"https://orcid.org/0000-0001-9364-9228"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongjun Liu","raw_affiliation_strings":["Hangzhou Dianzi University,School of Computer Science,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,School of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003649465","display_name":"Wanzeng Kong","orcid":"https://orcid.org/0000-0002-0113-6968"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanzeng Kong","raw_affiliation_strings":["Hangzhou Dianzi University,School of Computer Science,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Dianzi University,School of Computer Science,Hangzhou,China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":1.1979,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.78935381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9624999761581421,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10828","display_name":"Biometric Identification and Security","score":0.9624999761581421,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T13192","display_name":"Forensic Fingerprint Detection Methods","score":0.9297000169754028,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7702782154083252},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6793314218521118},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5748418569564819},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5300214886665344},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3813840448856354},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33053913712501526}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7702782154083252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6793314218521118},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5748418569564819},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5300214886665344},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3813840448856354},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33053913712501526}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme57554.2024.10688012","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme57554.2024.10688012","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2101946599","https://openalex.org/W2132360759","https://openalex.org/W2162800060","https://openalex.org/W2247338144","https://openalex.org/W2559463885","https://openalex.org/W2741907166","https://openalex.org/W2744802016","https://openalex.org/W2786768213","https://openalex.org/W2823613278","https://openalex.org/W2914797429","https://openalex.org/W2930980596","https://openalex.org/W2963542245","https://openalex.org/W2963857521","https://openalex.org/W3035006120","https://openalex.org/W3044950289","https://openalex.org/W3095862531","https://openalex.org/W3162031981","https://openalex.org/W4200550087","https://openalex.org/W4220692749","https://openalex.org/W4293363567","https://openalex.org/W4293846201","https://openalex.org/W4320804538","https://openalex.org/W4366250326","https://openalex.org/W6640425456","https://openalex.org/W6734312481"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3009238340","https://openalex.org/W3116076068","https://openalex.org/W2229312674"],"abstract_inverted_index":{"EEG-based":[0],"brainprint":[1],"recognition":[2],"with":[3,26],"deep":[4,23],"learning":[5,24],"models":[6,25],"has":[7],"garnered":[8],"much":[9],"attention":[10],"in":[11,22,82,98,128],"biometric":[12],"identification.":[13],"Yet,":[14],"studies":[15],"have":[16],"indicated":[17],"vulnerability":[18],"to":[19,138],"adversarial":[20,36,73],"attacks":[21,41],"EEG":[27,45,60],"inputs.":[28],"In":[29,123],"this":[30],"paper,":[31],"we":[32],"introduce":[33],"a":[34,86],"novel":[35],"attack":[37,90,110,114],"method":[38,63,111,134],"that":[39,108],"jointly":[40],"time-domain":[42,59,70],"and":[43,92,101,104,119],"frequency-domain":[44,83],"signals":[46,130],"by":[47,132],"employing":[48],"wavelet":[49],"transform.":[50],"Different":[51],"from":[52,78],"most":[53],"existing":[54],"methods":[55],"which":[56],"only":[57,65],"target":[58],"signals,":[61],"our":[62,109,133],"not":[64],"takes":[66],"advantage":[67],"of":[68],"the":[69,79,105,124,126,129,139],"attack\u2019s":[71],"potent":[72],"strength":[74],"but":[75],"also":[76],"benefits":[77],"imperceptibility":[80],"inherent":[81],"attack,":[84],"achieving":[85],"better":[87],"balance":[88],"between":[89],"performance":[91,115],"imperceptibility.":[93],"Extensive":[94],"experiments":[95],"are":[96,135],"conducted":[97],"both":[99],"white-":[100],"grey-box":[102],"scenarios":[103],"results":[106],"demonstrate":[107],"achieves":[112],"state-of-the-art":[113],"on":[116],"three":[117,120],"datasets":[118],"deep-learning":[121],"models.":[122],"meanwhile,":[125],"perturbations":[127],"attacked":[131],"barely":[136],"perceptible":[137],"human":[140],"visual":[141],"system.":[142]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}