{"id":"https://openalex.org/W4416707193","doi":"https://doi.org/10.1109/lsp.2025.3637716","title":"Enhancing the Transferability of Adversarial Attacks for Remote Sensing Scene Classification via Asymmetric Frequency Mixing","display_name":"Enhancing the Transferability of Adversarial Attacks for Remote Sensing Scene Classification via Asymmetric Frequency Mixing","publication_year":2025,"publication_date":"2025-11-26","ids":{"openalex":"https://openalex.org/W4416707193","doi":"https://doi.org/10.1109/lsp.2025.3637716"},"language":null,"primary_location":{"id":"doi:10.1109/lsp.2025.3637716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3637716","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5013140663","display_name":"Lizhuo Liu","orcid":"https://orcid.org/0000-0001-5488-4671"},"institutions":[{"id":"https://openalex.org/I110440473","display_name":"Xi'an University of Science and Technology","ror":"https://ror.org/046fkpt18","country_code":"CN","type":"education","lineage":["https://openalex.org/I110440473"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizhuo Liu","raw_affiliation_strings":["School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-5488-4671","affiliations":[{"raw_affiliation_string":"School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, China","institution_ids":["https://openalex.org/I110440473"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035606208","display_name":"Yuefeng Bai","orcid":"https://orcid.org/0000-0002-2592-3691"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuefeng Bai","raw_affiliation_strings":["School of Automation, Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069081429","display_name":"Yimin Fu","orcid":"https://orcid.org/0000-0003-2070-995X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yimin Fu","raw_affiliation_strings":["School of Automation, Northwestern Polytechnical University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-2070-995X","affiliations":[{"raw_affiliation_string":"School of Automation, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2859,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.86725614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"33","issue":null,"first_page":"121","last_page":"125"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9753000140190125,"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":0.9753000140190125,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.005200000014156103,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.002199999988079071,"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/adversarial-system","display_name":"Adversarial system","score":0.7049000263214111},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.6891999840736389},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6001999974250793},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5465999841690063},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.475600004196167},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.4406999945640564},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.39320001006126404}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7878000140190125},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7049000263214111},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.6891999840736389},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6001999974250793},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5465999841690063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5016000270843506},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.4406999945640564},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.38519999384880066},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.373199999332428},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.36160001158714294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3465000092983246},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29829999804496765},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.271699994802475},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3637716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3637716","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1980038761","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2294802479","https://openalex.org/W2592962403","https://openalex.org/W2962847335","https://openalex.org/W2962858109","https://openalex.org/W2963384482","https://openalex.org/W2963446712","https://openalex.org/W2963542245","https://openalex.org/W3108341030","https://openalex.org/W3171288285","https://openalex.org/W4246193833","https://openalex.org/W4296558684","https://openalex.org/W4312268651","https://openalex.org/W4312648479","https://openalex.org/W4313141826","https://openalex.org/W4401567578","https://openalex.org/W4402754002","https://openalex.org/W4404688148","https://openalex.org/W4407937888","https://openalex.org/W4408609828","https://openalex.org/W4413156417","https://openalex.org/W7077544053"],"related_works":[],"abstract_inverted_index":{"Adversarial":[0],"attacks":[1],"serve":[2],"as":[3],"an":[4,145],"efficient":[5],"approach":[6],"to":[7,27,113,115,128,170,183,202],"reveal":[8],"potential":[9],"weaknesses":[10],"of":[11,39,66,92,109,155],"deep":[12],"neural":[13],"networks":[14],"(DNNs).":[15],"In":[16,88],"practice,":[17],"models":[18],"are":[19,140,168,177],"often":[20],"deployed":[21],"in":[22,73,149,179,206],"black-box":[23],"settings,":[24],"where":[25],"access":[26],"parameters":[28],"and":[29,217],"architectures":[30],"is":[31,48,196],"restricted.":[32],"This":[33],"imposes":[34],"requirements":[35],"on":[36,43,79,198,212],"the":[37,44,64,90,97,107,150,156,164,199,213,222],"transferability":[38,111,131],"adversarial":[40,110,130],"examples":[41],"crafted":[42],"source":[45],"model,":[46],"which":[47,83],"typically":[49,77],"enhanced":[50],"by":[51,142,163],"simulating":[52],"diverse":[53],"input":[54,57],"patterns":[55,139],"through":[56],"transformations":[58],"during":[59],"gradient":[60],"computation.":[61],"However,":[62],"given":[63],"lack":[65],"a":[67,180,192],"clear":[68],"foreground-background":[69],"distinction,":[70],"scene":[71,135,158],"classification":[72],"remote":[74,133],"sensing":[75,134],"images":[76,154,201],"relies":[78],"global":[80],"contextual":[81],"understanding,":[82],"exhibits":[84],"substantial":[85],"inter-model":[86,204],"variation.":[87],"addition,":[89],"diversity":[91],"geospatial":[93,208],"structures":[94],"further":[95],"amplifies":[96],"discrepancies":[98,205],"across":[99,229],"different":[100,230],"model":[101,231],"architectures.":[102,232],"These":[103],"factors":[104],"jointly":[105],"impede":[106],"enhancement":[108],"due":[112],"overfitting":[114],"model-specific":[116,185],"information.":[117,190],"To":[118],"address":[119],"this":[120],"challenge,":[121],"we":[122],"propose":[123],"asymmetric":[124],"frequency":[125,151],"mixing":[126,147],"(AFM)":[127],"improve":[129],"for":[132],"classification.":[136],"Specifically,":[137],"context":[138],"simulated":[141],"selectively":[143],"employing":[144],"asymmetrical":[146],"operation":[148,195],"domain.":[152],"For":[153],"same":[157],"category,":[159],"low-frequency":[160],"components":[161,176],"decomposed":[162],"discrete":[165],"wavelet":[166],"transform":[167],"mixed":[169,178],"enrich":[171],"semantic-related":[172],"diversity.":[173],"Meanwhile,":[174],"high-frequency":[175],"class-agnostic":[181],"fashion":[182],"suppress":[184],"bias":[186],"toward":[187],"background":[188],"texture":[189],"Moreover,":[191],"block":[193],"shuffling":[194],"imposed":[197],"reconstructed":[200],"reduce":[203],"capturing":[207],"structures.":[209],"Extensive":[210],"experiments":[211],"UCMerced":[214],"Land":[215],"Use":[216],"SIRI-WHU":[218],"datasets":[219],"demonstrate":[220],"that":[221],"proposed":[223],"method":[224],"achieves":[225],"state-of-the-art":[226],"attack":[227],"performance":[228]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-18T07:39:51.176621","created_date":"2025-11-27T00:00:00"}