{"id":"https://openalex.org/W4405103485","doi":"https://doi.org/10.1109/milcom61039.2024.10773735","title":"Filtered Randomized Smoothing: A New Defense for Robust Modulation Classification","display_name":"Filtered Randomized Smoothing: A New Defense for Robust Modulation Classification","publication_year":2024,"publication_date":"2024-10-28","ids":{"openalex":"https://openalex.org/W4405103485","doi":"https://doi.org/10.1109/milcom61039.2024.10773735"},"language":"en","primary_location":{"id":"doi:10.1109/milcom61039.2024.10773735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom61039.2024.10773735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM)","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/A5101992900","display_name":"Wenhan Zhang","orcid":"https://orcid.org/0000-0001-5999-9283"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenhan Zhang","raw_affiliation_strings":["University of Arizona,Department of ECE,Tucson,AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona,Department of ECE,Tucson,AZ","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002572172","display_name":"Meiyu Zhong","orcid":"https://orcid.org/0000-0002-6014-9283"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meiyu Zhong","raw_affiliation_strings":["University of Arizona,Department of ECE,Tucson,AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona,Department of ECE,Tucson,AZ","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004316408","display_name":"Ravi Tandon","orcid":"https://orcid.org/0000-0002-6182-6098"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ravi Tandon","raw_affiliation_strings":["University of Arizona,Department of ECE,Tucson,AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona,Department of ECE,Tucson,AZ","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081934496","display_name":"Marwan Krunz","orcid":"https://orcid.org/0000-0001-7137-2985"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marwan Krunz","raw_affiliation_strings":["University of Arizona,Department of ECE,Tucson,AZ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona,Department of ECE,Tucson,AZ","institution_ids":["https://openalex.org/I138006243"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9164,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.80389956,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"789","last_page":"794"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9959999918937683,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9959999918937683,"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/T10662","display_name":"Ultrasonics and Acoustic Wave Propagation","score":0.9014999866485596,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.7496249079704285},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5653828382492065},{"id":"https://openalex.org/keywords/modulation","display_name":"Modulation (music)","score":0.5088198781013489},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36882898211479187},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3672933578491211},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.12849000096321106},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10841864347457886},{"id":"https://openalex.org/keywords/acoustics","display_name":"Acoustics","score":0.07702663540840149}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7496249079704285},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5653828382492065},{"id":"https://openalex.org/C123079801","wikidata":"https://www.wikidata.org/wiki/Q750240","display_name":"Modulation (music)","level":2,"score":0.5088198781013489},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36882898211479187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3672933578491211},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.12849000096321106},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10841864347457886},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.07702663540840149}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/milcom61039.2024.10773735","is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom61039.2024.10773735","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2773170971","https://openalex.org/W2968867107","https://openalex.org/W3112733201","https://openalex.org/W3213282580","https://openalex.org/W4206151643","https://openalex.org/W4226117697","https://openalex.org/W4226239489","https://openalex.org/W4293846201","https://openalex.org/W4295308277","https://openalex.org/W4313591247","https://openalex.org/W4391791430","https://openalex.org/W6640425456","https://openalex.org/W6758684365","https://openalex.org/W6764522968","https://openalex.org/W6771592004","https://openalex.org/W6869922378"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W1978572805","https://openalex.org/W2383807498","https://openalex.org/W1997992934","https://openalex.org/W1987225439","https://openalex.org/W4238188170","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Deep":[0],"Neural":[1],"Network":[2],"(DNN)":[3],"based":[4],"classifiers":[5,18,108,136],"have":[6,19],"recently":[7],"been":[8],"used":[9],"for":[10,137],"the":[11,39,78,101,127,138,197,203,215,229,234],"modulation":[12,107,135,204],"classification":[13,205],"of":[14,38,103,130,147,193,218,226,236,252],"RF":[15,149],"signals.":[16,259],"These":[17],"shown":[20],"impressive":[21],"performance":[22],"gains":[23],"relative":[24],"to":[25,32,59,94,158,202,209,213,232],"conventional":[26],"methods,":[27],"however,":[28,91],"they":[29,92],"are":[30,151],"vulnerable":[31],"imperceptible":[33],"(low-power)":[34],"adversarial":[35,44],"attacks.":[36,66],"Some":[37],"prominent":[40],"defense":[41,113,177],"approaches":[42],"include":[43],"training":[45],"(AT)":[46],"and":[47,238,248,257],"randomized":[48,184],"smoothing":[49],"(RS).":[50],"While":[51],"AT":[52,247],"increases":[53],"robustness":[54],"in":[55,162,250],"general,":[56],"it":[57,241],"fails":[58],"provide":[60,111,222],"resilience":[61],"against":[62,88,114],"previously":[63],"unseen":[64],"adaptive":[65],"Other":[67],"approaches,":[68],"such":[69],"as":[70,190],"Randomized":[71,172],"Smoothing":[72,173],"(RS),":[73],"which":[74,178],"injects":[75],"noise":[76],"into":[77],"input,":[79],"address":[80],"this":[81,97,122,166],"shortcoming":[82],"by":[83,195],"providing":[84,210],"provable":[85,112],"certified":[86,216],"guarantees":[87],"arbitrary":[89,115],"attacks,":[90],"tend":[93,157],"sacrifice":[95],"accuracy.In":[96],"paper,":[98],"we":[99,124,169,220],"study":[100],"problem":[102],"designing":[104],"robust":[105],"DNN-based":[106],"that":[109,144,240],"can":[110,187],"attacks":[116,133],"without":[117],"significantly":[118,242],"sacrificing":[119],"accuracy.":[120],"To":[121,164],"end,":[123],"first":[125],"analyze":[126],"spectral":[128,145,167,180],"content":[129],"commonly":[131],"studied":[132],"on":[134,228,254],"benchmark":[139],"RadioML":[140,230],"dataset.":[141],"We":[142],"observe":[143],"signatures":[146],"un-perturbed":[148],"signals":[150,156],"highly":[152],"localized,":[153],"whereas":[154],"attack":[155],"be":[159,188],"spread":[160],"out":[161],"frequency.":[163],"exploit":[165],"heterogeneity,":[168],"propose":[170],"Filtered":[171],"(FRS),":[174],"a":[175,191,223],"novel":[176],"combines":[179],"filtering":[181],"together":[182],"with":[183],"smoothing.":[185],"FRS":[186,237],"viewed":[189],"strengthening":[192],"RS":[194,249],"leveraging":[196],"specificity":[198],"(spectral":[199],"Heterogeneity)":[200],"inherent":[201],"problem.":[206],"In":[207],"addition":[208],"an":[211],"approach":[212],"compute":[214],"accuracy":[217,253],"FRS,":[219],"also":[221],"comprehensive":[224],"set":[225],"simulations":[227],"dataset":[231],"show":[233,239],"effectiveness":[235],"outperforms":[243],"existing":[244],"defenses":[245],"including":[246],"terms":[251],"both":[255],"attacked":[256],"benign":[258]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}