{"id":"https://openalex.org/W7117320514","doi":"https://doi.org/10.1109/tifs.2025.3648871","title":"Reinforcing Adversarial Transferability via Negative Class Guided Example Generation","display_name":"Reinforcing Adversarial Transferability via Negative Class Guided Example Generation","publication_year":2025,"publication_date":"2025-12-26","ids":{"openalex":"https://openalex.org/W7117320514","doi":"https://doi.org/10.1109/tifs.2025.3648871"},"language":null,"primary_location":{"id":"doi:10.1109/tifs.2025.3648871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2025.3648871","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"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 Transactions on Information Forensics and Security","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/A5041328137","display_name":"Hegui Zhu","orcid":"https://orcid.org/0000-0002-6501-4097"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hegui Zhu","raw_affiliation_strings":["Department of Mathematics, College of Sciences, Northeastern University, Shenyang, Liaoning, China"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, College of Sciences, Northeastern University, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029809100","display_name":"Wenqi Cui","orcid":"https://orcid.org/0000-0002-1295-4392"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqi Cui","raw_affiliation_strings":["Department of Mathematics, College of Sciences, Northeastern University, Shenyang, Liaoning, China"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, College of Sciences, Northeastern University, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100956204","display_name":"Yue Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Yan","raw_affiliation_strings":["School of Computer Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121308713","display_name":"Ning Han","orcid":null},"institutions":[{"id":"https://openalex.org/I4610292","display_name":"Xiangtan University","ror":"https://ror.org/00xsfaz62","country_code":"CN","type":"education","lineage":["https://openalex.org/I4610292"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Han","raw_affiliation_strings":["School of Computer Science, Xiangtan University, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Xiangtan University, Xiangtan, China","institution_ids":["https://openalex.org/I4610292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041328137"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.83983208,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":null,"first_page":"532","last_page":"546"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9927999973297119,"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":0.9927999973297119,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.00139999995008111,"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":0.0010000000474974513,"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":0.9279999732971191},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.7170000076293945},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5715000033378601},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5425999760627747},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5163000226020813},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.45989999175071716},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.43549999594688416}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9279999732971191},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.802299976348877},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.7170000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6261000037193298},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5715000033378601},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5163000226020813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4887000024318695},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.45989999175071716},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4081999957561493},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3337000012397766},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31630000472068787},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.26460000872612}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tifs.2025.3648871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2025.3648871","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"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 Transactions on Information Forensics and Security","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":42,"referenced_works":["https://openalex.org/W2117539524","https://openalex.org/W2117876524","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2549139847","https://openalex.org/W2603766943","https://openalex.org/W2774018344","https://openalex.org/W2774644650","https://openalex.org/W2962847335","https://openalex.org/W2962858109","https://openalex.org/W2963446712","https://openalex.org/W2963542245","https://openalex.org/W2963857521","https://openalex.org/W2969542116","https://openalex.org/W2984699060","https://openalex.org/W3034190247","https://openalex.org/W3097746859","https://openalex.org/W3127807678","https://openalex.org/W3138516171","https://openalex.org/W3171288285","https://openalex.org/W4214636423","https://openalex.org/W4214669216","https://openalex.org/W4225859735","https://openalex.org/W4293846201","https://openalex.org/W4304098213","https://openalex.org/W4308233767","https://openalex.org/W4323338544","https://openalex.org/W4386597255","https://openalex.org/W4390871946","https://openalex.org/W4390872540","https://openalex.org/W4390872679","https://openalex.org/W4390873564","https://openalex.org/W4394717653","https://openalex.org/W4395448418","https://openalex.org/W4400615477","https://openalex.org/W4400771068","https://openalex.org/W4402754002","https://openalex.org/W4403906474","https://openalex.org/W4406171890","https://openalex.org/W4409311375","https://openalex.org/W4409367996"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"studies":[1],"have":[2],"revealed":[3],"that":[4,71,154,179],"Deep":[5],"Neural":[6,193],"Networks":[7,194],"(DNNs)":[8],"are":[9,16,129],"highly":[10],"vulnerable":[11],"to":[12,22,26,100,132,163],"adversarial":[13,42,66,92,113,169,184],"examples,":[14,43],"which":[15],"generated":[17,141],"by":[18],"introducing":[19],"imperceptible":[20],"perturbations":[21],"clean":[23,127],"images,":[24],"leading":[25],"misclassification.":[27],"The":[28],"existing":[29,65],"untargeted":[30,112],"attack":[31,67,188,205],"usually":[32],"only":[33],"focuses":[34],"on":[35,53,174,190],"weakening":[36],"the":[37,45,54,57,64,72,77,81,86,91,105,123,126,133,137,160,165,175,183],"original":[38,106],"class":[39,157],"when":[40],"generating":[41],"ignoring":[44],"model\u2019s":[46],"prediction":[47],"distribution":[48],"for":[49],"other":[50],"classes.":[51],"Based":[52],"analysis":[55],"of":[56,60,76,125,168,186],"attention":[58],"heatmap":[59],"model":[61,87],"decision":[62],"and":[63,89,144,196],"results,":[68],"we":[69,109],"find":[70],"high-confidence":[73],"negative":[74,156],"classes":[75],"images":[78],"often":[79],"reflect":[80],"natural":[82],"weak":[83],"directions":[84],"in":[85,203],"decision,":[88],"updating":[90],"examples":[93],"along":[94],"this":[95],"direction":[96,167],"is":[97,140,152],"more":[98],"likely":[99],"help":[101],"it":[102],"deviate":[103],"from":[104],"class.":[107],"Therefore,":[108],"propose":[110],"an":[111],"example":[114],"generation":[115],"method":[116],"via":[117,142],"Negative":[118],"Class":[119],"Guidance":[120],"(NCG).":[121],"First,":[122],"logits":[124],"image":[128],"extracted":[130],"according":[131],"classification":[134],"confidence.":[135],"Second,":[136],"soft":[138,161],"label":[139,162],"smoothing":[143],"normalization":[145],"operations.":[146],"Finally,":[147],"a":[148],"novel":[149],"loss":[150],"function":[151],"derived":[153],"integrates":[155],"information":[158],"with":[159],"guide":[164],"update":[166],"examples.":[170],"Extensive":[171],"experiments":[172],"conducted":[173],"ImageNet":[176],"dataset":[177],"demonstrate":[178],"NCG":[180],"substantially":[181],"enhances":[182],"transferability":[185],"state-of-the-art":[187],"methodologies":[189],"both":[191],"Convolutional":[192],"(CNNs)":[195],"Vision":[197],"Transformers":[198],"(ViTs),":[199],"highlighting":[200],"its":[201],"effectiveness":[202],"black-box":[204],"scenarios.":[206]},"counts_by_year":[],"updated_date":"2026-01-08T20:05:33.558190","created_date":"2025-12-26T00:00:00"}
