{"id":"https://openalex.org/W4315630185","doi":"https://doi.org/10.1109/globecom48099.2022.10000826","title":"Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm","display_name":"Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4315630185","doi":"https://doi.org/10.1109/globecom48099.2022.10000826"},"language":"en","primary_location":{"id":"doi:10.1109/globecom48099.2022.10000826","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10000826","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","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/A5100382963","display_name":"Xinyi Wang","orcid":"https://orcid.org/0000-0003-2658-8650"},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]},{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Xinyi Wang","raw_affiliation_strings":["University of Queensland,School of Info. Tech and Elec. Engr.,Brisbane,Australia","School of Info. Tech and Elec. Engr., University of Queensland, Brisbane, Australia"],"affiliations":[{"raw_affiliation_string":"University of Queensland,School of Info. Tech and Elec. Engr.,Brisbane,Australia","institution_ids":["https://openalex.org/I160993911"]},{"raw_affiliation_string":"School of Info. Tech and Elec. Engr., University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069316253","display_name":"Simon Yusuf Enoch","orcid":"https://orcid.org/0000-0002-0970-3621"},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]},{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Simon Yusuf Enoch","raw_affiliation_strings":["University of Queensland,School of Info. Tech and Elec. Engr.,Brisbane,Australia","School of Info. Tech and Elec. Engr., University of Queensland, Brisbane, Australia"],"affiliations":[{"raw_affiliation_string":"University of Queensland,School of Info. Tech and Elec. Engr.,Brisbane,Australia","institution_ids":["https://openalex.org/I160993911"]},{"raw_affiliation_string":"School of Info. Tech and Elec. Engr., University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019789320","display_name":"Dong Seong Kim","orcid":"https://orcid.org/0000-0003-2605-187X"},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]},{"id":"https://openalex.org/I165143802","display_name":"The University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dan Dongseong Kim","raw_affiliation_strings":["University of Queensland,School of Info. Tech and Elec. Engr.,Brisbane,Australia","School of Info. Tech and Elec. Engr., University of Queensland, Brisbane, Australia"],"affiliations":[{"raw_affiliation_string":"University of Queensland,School of Info. Tech and Elec. Engr.,Brisbane,Australia","institution_ids":["https://openalex.org/I160993911"]},{"raw_affiliation_string":"School of Info. Tech and Elec. Engr., University of Queensland, Brisbane, Australia","institution_ids":["https://openalex.org/I165143802"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100382963"],"corresponding_institution_ids":["https://openalex.org/I160993911","https://openalex.org/I165143802"],"apc_list":null,"apc_paid":null,"fwci":0.2092,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.45879048,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"80","last_page":"85"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9477999806404114,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9348999857902527,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.916475772857666},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8855177164077759},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.699491024017334},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6891599893569946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6019768118858337},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4835110008716583},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.48317351937294006},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47755488753318787},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46890485286712646},{"id":"https://openalex.org/keywords/semantic-space","display_name":"Semantic space","score":0.4339093267917633},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4262464940547943},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4216534495353699},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.41786548495292664},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3411766290664673}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.916475772857666},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8855177164077759},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.699491024017334},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6891599893569946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6019768118858337},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4835110008716583},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.48317351937294006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47755488753318787},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46890485286712646},{"id":"https://openalex.org/C2986420190","wikidata":"https://www.wikidata.org/wiki/Q39045939","display_name":"Semantic space","level":2,"score":0.4339093267917633},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4262464940547943},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4216534495353699},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.41786548495292664},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3411766290664673},{"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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom48099.2022.10000826","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/globecom48099.2022.10000826","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","display_name":"No poverty","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2535873859","https://openalex.org/W2943671295","https://openalex.org/W2962718684","https://openalex.org/W2963366334","https://openalex.org/W2963542245","https://openalex.org/W2963857521","https://openalex.org/W2963859254","https://openalex.org/W3088909400","https://openalex.org/W3214938888","https://openalex.org/W4288257146","https://openalex.org/W4288296135","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6719080892","https://openalex.org/W6764668024","https://openalex.org/W6766401332","https://openalex.org/W6781550413"],"related_works":["https://openalex.org/W4394785709","https://openalex.org/W4296978181","https://openalex.org/W2912987408","https://openalex.org/W2937381246","https://openalex.org/W3004801820","https://openalex.org/W4281672036","https://openalex.org/W4313444753","https://openalex.org/W4230582276","https://openalex.org/W2112408611","https://openalex.org/W3181580843"],"abstract_inverted_index":{"Widely":[0],"used":[1],"deep":[2],"learning":[3],"models":[4,16],"are":[5],"found":[6,104],"to":[7,38,89],"have":[8],"poor":[9],"robustness.":[10],"Little":[11],"noises":[12,85],"can":[13,48],"fool":[14],"state-of-the-art":[15],"into":[17],"making":[18],"incorrect":[19],"predictions.":[20],"While":[21],"there":[22],"is":[23],"a":[24,64,109,115],"great":[25],"deal":[26],"of":[27,33,53,69,76,93,125],"high-performance":[28],"attack":[29,117],"generation":[30],"methods,":[31],"most":[32],"them":[34,43],"directly":[35],"add":[36],"perturbations":[37,107],"original":[39,71],"data":[40,77,124],"and":[41,100,103,127],"measure":[42,84],"using":[44,108],"L_p":[45],"norms;":[46],"this":[47,60],"break":[49],"the":[50,91,121],"major":[51],"structure":[52],"data,":[54,72],"thus,":[55],"creating":[56],"invalid":[57],"attacks.":[58],"In":[59],"paper,":[61],"we":[62,83],"propose":[63],"black-box":[65],"attack,":[66],"which,":[67],"instead":[68],"modifying":[70],"modifies":[73],"latent":[74],"features":[75],"extracted":[78],"by":[79],"an":[80],"autoencoder;":[81],"then,":[82],"in":[86],"semantic":[87],"space":[88],"protect":[90],"semantics":[92],"data.":[94],"We":[95],"trained":[96],"autoencoders":[97],"on":[98,120],"MNIST":[99,126],"CIFAR-10":[101,128],"datasets":[102,129],"optimal":[105],"adversarial":[106],"genetic":[110],"algorithm.":[111],"Our":[112],"approach":[113],"achieved":[114],"100%":[116],"success":[118],"rate":[119],"first":[122],"100":[123],"with":[130],"less":[131],"perturbation":[132],"than":[133],"FGSM.":[134]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}