{"id":"https://openalex.org/W4372260262","doi":"https://doi.org/10.1109/icassp49357.2023.10096549","title":"Similarity Relation Preserving Cross-Modal Learning for Multispectral Pedestrian Detection Against Adversarial Attacks","display_name":"Similarity Relation Preserving Cross-Modal Learning for Multispectral Pedestrian Detection Against Adversarial Attacks","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372260262","doi":"https://doi.org/10.1109/icassp49357.2023.10096549"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10096549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10096549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5036936141","display_name":"Jung Uk Kim","orcid":"https://orcid.org/0000-0003-4533-4875"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]},{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jung Uk Kim","raw_affiliation_strings":["KAIST,Image and Video Systems Lab, School of Electrical Engineering,South Korea","Department of Computer Science and Engineering, Kyung Hee University, South Korea","Image and Video Systems Lab, School of Electrical Engineering, KAIST, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST,Image and Video Systems Lab, School of Electrical Engineering,South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, Kyung Hee University, South Korea","institution_ids":["https://openalex.org/I35928602"]},{"raw_affiliation_string":"Image and Video Systems Lab, School of Electrical Engineering, KAIST, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038798134","display_name":"Yong Man Ro","orcid":"https://orcid.org/0000-0001-5306-6853"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yong Man Ro","raw_affiliation_strings":["KAIST,Image and Video Systems Lab, School of Electrical Engineering,South Korea","Image and Video Systems Lab, School of Electrical Engineering, KAIST, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KAIST,Image and Video Systems Lab, School of Electrical Engineering,South Korea","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"Image and Video Systems Lab, School of Electrical Engineering, KAIST, South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9986000061035156,"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":0.9986000061035156,"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":0.9944000244140625,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9825999736785889,"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":0.8517392873764038},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.730101466178894},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.7160629034042358},{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.7077184319496155},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6905649900436401},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.654193639755249},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.599031925201416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5869439244270325},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4138231873512268},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39949312806129456},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3732759356498718},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.325563907623291},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.32541441917419434},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2294706106185913},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11803469061851501}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8517392873764038},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.730101466178894},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7160629034042358},{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7077184319496155},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6905649900436401},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.654193639755249},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.599031925201416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5869439244270325},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4138231873512268},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39949312806129456},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3732759356498718},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.325563907623291},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.32541441917419434},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2294706106185913},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11803469061851501},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49357.2023.10096549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10096549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1910108985","https://openalex.org/W1945616565","https://openalex.org/W2058757422","https://openalex.org/W2060565253","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2415234561","https://openalex.org/W2565639579","https://openalex.org/W2570685808","https://openalex.org/W2585724928","https://openalex.org/W2604505099","https://openalex.org/W2791953061","https://openalex.org/W2889698616","https://openalex.org/W2913266441","https://openalex.org/W2963188557","https://openalex.org/W2963857521","https://openalex.org/W2985966260","https://openalex.org/W2987131085","https://openalex.org/W3009542902","https://openalex.org/W3027535223","https://openalex.org/W3158128549","https://openalex.org/W3202329597","https://openalex.org/W4224933794","https://openalex.org/W4224936528","https://openalex.org/W4283820844","https://openalex.org/W4293846201","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6731927902","https://openalex.org/W6739868092","https://openalex.org/W6748965907","https://openalex.org/W6759129252","https://openalex.org/W6774469542","https://openalex.org/W6777987809"],"related_works":["https://openalex.org/W3132270449","https://openalex.org/W4377289091","https://openalex.org/W2972620127","https://openalex.org/W3013647784","https://openalex.org/W2981141433","https://openalex.org/W2997281059","https://openalex.org/W2802018156","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2922437833"],"abstract_inverted_index":{"Although":[0],"multispectral":[1,57,118],"pedestrian":[2],"detection":[3,8],"studies":[4],"have":[5],"shown":[6],"remarkable":[7],"performances,":[9],"they":[10],"are":[11],"still":[12],"vulnerable":[13],"to":[14,67,78,94,102,105],"adversarial":[15,31,54,100,127,139],"attacks.":[16,128],"We":[17],"see":[18],"the":[19,30,48,69,79,96,108,113,138],"similarity":[20,49,71,97],"relations":[21],"between":[22,51,73,99,115,122],"object":[23],"candidates":[24,52,75,101],"were":[25],"not":[26],"maintained":[27],"because":[28],"of":[29,107],"attacks,":[32],"resulting":[33],"in":[34,126],"performance":[35],"degradation.":[36],"In":[37],"this":[38],"paper,":[39],"we":[40,60,86],"introduce":[41],"a":[42],"new":[43],"method":[44,135],"that":[45,106,133],"can":[46,120],"preserve":[47],"relation":[50,72,98],"against":[53],"attacks":[55],"using":[56],"knowledge.":[58],"First,":[59],"propose":[61,87],"Similarity":[62,89],"Relation":[63,90],"Generation":[64],"(SRG)":[65],"module":[66,93],"generate":[68],"optimal":[70],"clean":[74,109],"by":[76],"referring":[77],"two":[80],"modalities":[81],"(color":[82],"and":[83],"thermal).":[84],"Second,":[85],"Adversarial":[88],"Preserving":[91],"(ASRP)":[92],"guide":[95],"be":[103],"similar":[104],"candidates.":[110],"By":[111],"maintaining":[112],"relationship":[114],"candidates,":[116],"our":[117,134],"detector":[119],"distinguish":[121],"pedestrian/background":[123],"classes":[124],"even":[125],"Comprehensive":[129],"experimental":[130],"results":[131],"show":[132],"conspicuously":[136],"improves":[137],"robustness.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-26T07:53:14.480251","created_date":"2025-10-10T00:00:00"}