{"id":"https://openalex.org/W3177448524","doi":"https://doi.org/10.1109/lsp.2021.3090944","title":"DiFNet: Densely High-Frequency Convolutional Neural Networks","display_name":"DiFNet: Densely High-Frequency Convolutional Neural Networks","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3177448524","doi":"https://doi.org/10.1109/lsp.2021.3090944","mag":"3177448524"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2021.3090944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3090944","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/A5024664086","display_name":"Wenzheng Hu","orcid":"https://orcid.org/0000-0002-1115-8175"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenzheng Hu","raw_affiliation_strings":["The State Key Laboratory of Automotive Satefy and Energy, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Automotive Satefy and Energy, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443403","display_name":"Mingyang Li","orcid":"https://orcid.org/0000-0001-5973-7574"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyang Li","raw_affiliation_strings":["Insitute for Artificial Intelligence Tsinghua University (THUAI) State Key Lab of Intelligent Technologies and Systems Beijing, National Research Center for Infomation Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Insitute for Artificial Intelligence Tsinghua University (THUAI) State Key Lab of Intelligent Technologies and Systems Beijing, National Research Center for Infomation Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401045","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0001-6157-0662"},"institutions":[{"id":"https://openalex.org/I4210150151","display_name":"Tsinghua Holdings (China)","ror":"https://ror.org/04sbypa54","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210150151"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["KE Holdings Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"KE Holdings Inc., Beijing, China","institution_ids":["https://openalex.org/I4210150151"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100436368","display_name":"Jianqiang Wang","orcid":"https://orcid.org/0000-0003-4363-6108"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianqiang Wang","raw_affiliation_strings":["The State Key Laboratory of Automotive Satefy and Energy, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"The State Key Laboratory of Automotive Satefy and Energy, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065063835","display_name":"Changshui Zhang","orcid":"https://orcid.org/0000-0002-8088-367X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changshui Zhang","raw_affiliation_strings":["Insitute for Artificial Intelligence Tsinghua University (THUAI) State Key Lab of Intelligent Technologies and Systems Beijing, National Research Center for Infomation Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Insitute for Artificial Intelligence Tsinghua University (THUAI) State Key Lab of Intelligent Technologies and Systems Beijing, National Research Center for Infomation Science and Technology (BNRist), Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5024664086"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.6998,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.75981721,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"28","issue":null,"first_page":"1340","last_page":"1344"},"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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9757000207901001,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.9136000275611877,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.8016715049743652},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7443009614944458},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7400622367858887},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7343142032623291},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6242384910583496},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.5012826919555664},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4905242323875427},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.48139405250549316},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4585336148738861},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.45532679557800293},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3405475318431854},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.17443031072616577}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8016715049743652},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7443009614944458},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7400622367858887},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7343142032623291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6242384910583496},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.5012826919555664},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4905242323875427},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.48139405250549316},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4585336148738861},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.45532679557800293},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3405475318431854},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.17443031072616577},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2021.3090944","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3090944","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":[{"id":"https://metadata.un.org/sdg/16","score":0.75,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G3251521753","display_name":null,"funder_award_id":"2020M680563","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G3816082249","display_name":null,"funder_award_id":"51625503","funder_id":"https://openalex.org/F4320336125","funder_display_name":"National Science Fund for Distinguished Young Scholars"}],"funders":[{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320336125","display_name":"National Science Fund for Distinguished Young Scholars","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W599071398","https://openalex.org/W1883420340","https://openalex.org/W1945616565","https://openalex.org/W1993962620","https://openalex.org/W2145023731","https://openalex.org/W2243397390","https://openalex.org/W2262888510","https://openalex.org/W2334297625","https://openalex.org/W2535873859","https://openalex.org/W2541674938","https://openalex.org/W2561498661","https://openalex.org/W2570685808","https://openalex.org/W2593892853","https://openalex.org/W2603766943","https://openalex.org/W2612445135","https://openalex.org/W2620038827","https://openalex.org/W2745565856","https://openalex.org/W2755956102","https://openalex.org/W2765424254","https://openalex.org/W2774018344","https://openalex.org/W2774644650","https://openalex.org/W2786118190","https://openalex.org/W2799004851","https://openalex.org/W2913266441","https://openalex.org/W2916286792","https://openalex.org/W2950106672","https://openalex.org/W2950468330","https://openalex.org/W2962729158","https://openalex.org/W2962872506","https://openalex.org/W2963001136","https://openalex.org/W2963143631","https://openalex.org/W2963154688","https://openalex.org/W2963207607","https://openalex.org/W2963361074","https://openalex.org/W2963389226","https://openalex.org/W2963410064","https://openalex.org/W2963446712","https://openalex.org/W2963542245","https://openalex.org/W2963612069","https://openalex.org/W2963744840","https://openalex.org/W2963857521","https://openalex.org/W2964197269","https://openalex.org/W2964253222","https://openalex.org/W2966108112","https://openalex.org/W2970317235","https://openalex.org/W2996564870","https://openalex.org/W3013195703","https://openalex.org/W3088909400","https://openalex.org/W3091870967","https://openalex.org/W3103557498","https://openalex.org/W4288363925","https://openalex.org/W4293350742","https://openalex.org/W4293846201","https://openalex.org/W4297775537","https://openalex.org/W4300511536","https://openalex.org/W4302278417","https://openalex.org/W6639568328","https://openalex.org/W6640425456","https://openalex.org/W6719080892","https://openalex.org/W6729059855","https://openalex.org/W6729756640","https://openalex.org/W6731039222","https://openalex.org/W6731927902","https://openalex.org/W6734787559","https://openalex.org/W6737664043","https://openalex.org/W6739868092","https://openalex.org/W6744511767","https://openalex.org/W6744890726","https://openalex.org/W6745272055","https://openalex.org/W6747920752","https://openalex.org/W6748204703","https://openalex.org/W6748475379","https://openalex.org/W6759129252","https://openalex.org/W6759204839","https://openalex.org/W6759334943","https://openalex.org/W6761100157","https://openalex.org/W6761833289","https://openalex.org/W6767228955","https://openalex.org/W6771809012","https://openalex.org/W6783920880"],"related_works":["https://openalex.org/W3048732067","https://openalex.org/W4383468834","https://openalex.org/W4384648009","https://openalex.org/W4303645823","https://openalex.org/W4285263558","https://openalex.org/W2900159906","https://openalex.org/W4287828318","https://openalex.org/W2406556600","https://openalex.org/W4283221438","https://openalex.org/W2899811703"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2,79],"networks":[3],"have":[4],"achieved":[5],"great":[6],"success":[7],"in":[8,48,58,110,134],"many":[9],"computer":[10],"vision":[11],"tasks.":[12],"However,":[13],"they":[14],"can":[15],"be":[16],"attacked":[17],"by":[18],"adversarial":[19,37,56,90,125,144],"examples":[20,38,57],"which":[21],"are":[22],"input-data":[23],"with":[24,139],"small":[25],"intentional":[26],"feature":[27],"perturbations":[28,121],"to":[29,36,43,63,86,102,116],"fool":[30],"machine":[31],"learning":[32],"models.":[33],"This":[34],"vulnerability":[35],"poses":[39],"a":[40,72],"potential":[41],"threat":[42],"their":[44],"widespread":[45],"application,":[46],"especially":[47],"security-sensitive":[49],"scenarios.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54],"revisit":[55],"the":[59,64,99,104,111,119],"frequency":[60],"domain":[61],"referring":[62],"computational":[65],"theory":[66],"of":[67,136],"edge":[68,95],"detection,":[69],"and":[70],"propose":[71],"novel":[73],"<italic":[74,82],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[75,83],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">densely":[76],"high-frequency":[77,108],"convolution":[78],"network</i>":[80],"(":[81],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">DiFNet</i>":[84],")":[85],"effectively":[87],"defend":[88,117],"against":[89,118,142],"attacks.":[91],"DiFNet":[92,130],"introduces":[93],"classical":[94],"detection":[96,105],"operations":[97],"into":[98],"network":[100],"structure":[101],"enhance":[103],"ability":[106],"for":[107],"components":[109],"image.":[112],"It":[113],"works":[114],"well":[115],"imperceptible":[120],"attacks":[122,145],"even":[123],"without":[124],"examples.":[126],"Experiments":[127],"demonstrate":[128],"that":[129],"outperforms":[131],"handcraft-designed":[132],"CNNs":[133],"terms":[135],"prediction":[137],"accuracy":[138],"improved":[140],"robustness":[141],"state-of-the-art":[143],"(FGSM,":[146],"PGD,":[147],"etc.).":[148]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}