{"id":"https://openalex.org/W4416252174","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227274","title":"Improving Fast Adversarial Training with Adversarial Sample Fusion","display_name":"Improving Fast Adversarial Training with Adversarial Sample Fusion","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416252174","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227274"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5100661116","display_name":"Chunlong Fan","orcid":"https://orcid.org/0000-0001-8127-6292"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunlong Fan","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066551989","display_name":"J. F. Sun","orcid":"https://orcid.org/0000-0003-4742-4292"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiru Sun","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101734838","display_name":"Xu Li","orcid":"https://orcid.org/0000-0002-7788-2414"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Xu","raw_affiliation_strings":["Shenyang Aerospace University,School of Computer Science,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University,School of Computer Science,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100661116"],"corresponding_institution_ids":["https://openalex.org/I125904092"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19515393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.967199981212616,"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":0.967199981212616,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.00860000029206276,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0052999998442828655,"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":0.8677999973297119},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8471999764442444},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6974999904632568},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47850000858306885},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4740999937057495},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.460099995136261},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.41429999470710754}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8677999973297119},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8471999764442444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7016000151634216},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6974999904632568},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6473000049591064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6114000082015991},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.460099995136261},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.41429999470710754},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C5465570","wikidata":"https://www.wikidata.org/wiki/Q5326898","display_name":"Early stopping","level":3,"score":0.3156999945640564},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2840000092983246},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.26409998536109924}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2618530766","https://openalex.org/W2962719052","https://openalex.org/W2963857521","https://openalex.org/W3034537217","https://openalex.org/W3035005405","https://openalex.org/W3090787324","https://openalex.org/W3107235539","https://openalex.org/W3206581298","https://openalex.org/W4226110825","https://openalex.org/W4403923000"],"related_works":[],"abstract_inverted_index":{"Adversarial":[0],"training":[1,39,97,127,162],"(AT)":[2],"is":[3],"a":[4,60,108,125],"fundamental":[5],"approach":[6,132],"for":[7],"enhancing":[8],"the":[9,21,45,90,101,105],"robustness":[10,65,160],"of":[11,92],"deep":[12],"neural":[13],"networks.":[14],"However,":[15,41],"its":[16],"main":[17],"limitation":[18],"lies":[19],"in":[20,63,158],"substantial":[22],"computational":[23],"cost":[24],"required":[25],"to":[26,53,59,95,113,124],"generate":[27],"adversarial":[28,38,93],"examples.":[29],"To":[30,72],"mitigate":[31],"this":[32,73,75],"issue,":[33],"researchers":[34],"have":[35],"proposed":[36],"single-step":[37],"methods.":[40],"methods":[42,157],"such":[43],"as":[44],"Fast":[46],"Gradient":[47],"Sign":[48],"Method":[49],"(FGSM)":[50],"are":[51,122],"prone":[52],"catastrophic":[54],"overfitting":[55,121],"(CO),":[56],"which":[57,88],"leads":[58],"sharp":[61],"decline":[62],"model":[64,96],"during":[66],"training,":[67],"limiting":[68],"their":[69],"practical":[70],"applicability.":[71],"end,":[74],"paper":[76],"proposes":[77],"an":[78,84],"improved":[79],"algorithm":[80,106],"(FGSM-AWA)":[81],"based":[82],"on":[83,135],"adaptive":[85],"fusion":[86,102],"mechanism,":[87],"balances":[89],"contribution":[91],"examples":[94],"by":[98,120],"dynamically":[99],"adjusting":[100],"factor.":[103],"Additionally,":[104],"incorporates":[107],"specialized":[109],"loss":[110],"function":[111],"designed":[112],"combat":[114],"CO,":[115],"ensuring":[116],"that":[117,153],"models":[118],"affected":[119],"restored":[123],"stable":[126],"state.":[128],"We":[129],"validate":[130],"our":[131],"through":[133],"experiments":[134],"CIFAR-10,":[136],"CIFAR-100":[137],"and":[138,148,161],"Tiny":[139],"ImageNet":[140],"datasets,":[141],"employing":[142],"three":[143],"network":[144],"architectures:":[145],"ResNet-18,":[146],"WideResNet-34":[147],"PreActResNet-18.":[149],"Experimental":[150],"results":[151],"demonstrate":[152],"FGSM-AWA":[154],"outperforms":[155],"baseline":[156],"both":[159],"efficiency.":[163]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}