{"id":"https://openalex.org/W4376852397","doi":"https://doi.org/10.1145/3573942.3574026","title":"A Model robustness optimization method based on adversarial sample detection","display_name":"A Model robustness optimization method based on adversarial sample detection","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4376852397","doi":"https://doi.org/10.1145/3573942.3574026"},"language":"en","primary_location":{"id":"doi:10.1145/3573942.3574026","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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/A5061298622","display_name":"Jiaze Sun","orcid":"https://orcid.org/0000-0001-6610-8653"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiaze Sun","raw_affiliation_strings":["Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0001-6610-8653","affiliations":[{"raw_affiliation_string":"Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001747562","display_name":"S. Long","orcid":"https://orcid.org/0000-0002-3243-1118"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuan Long","raw_affiliation_strings":["Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-3243-1118","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071087564","display_name":"Xianyan Ma","orcid":"https://orcid.org/0000-0002-0238-7048"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianyan Ma","raw_affiliation_strings":["Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-0238-7048","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070006393","display_name":"Y.C. Tang","orcid":"https://orcid.org/0000-0002-7698-9770"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanmei Tang","raw_affiliation_strings":["Xi'an University of Posts &amp; Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-7698-9770","affiliations":[{"raw_affiliation_string":"Xi'an University of Posts &amp; Telecommunications, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061298622"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.1387,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58557323,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"304","last_page":"310"},"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":0.9678999781608582,"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/T11515","display_name":"Bacillus and Francisella bacterial research","score":0.9412999749183655,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.9498245716094971},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8411677479743958},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6882407069206238},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6584206819534302},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6340225338935852},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5897108316421509},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5565134882926941},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47438791394233704},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.45532962679862976},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4461801052093506},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40036213397979736},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.05195116996765137}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9498245716094971},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8411677479743958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6882407069206238},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6584206819534302},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6340225338935852},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5897108316421509},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5565134882926941},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47438791394233704},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.45532962679862976},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4461801052093506},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40036213397979736},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.05195116996765137},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573942.3574026","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573942.3574026","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","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":13,"referenced_works":["https://openalex.org/W2180612164","https://openalex.org/W2243397390","https://openalex.org/W2269778407","https://openalex.org/W2768346313","https://openalex.org/W2794284562","https://openalex.org/W2956875312","https://openalex.org/W2963403664","https://openalex.org/W2963542245","https://openalex.org/W2963857521","https://openalex.org/W2964082701","https://openalex.org/W3035005405","https://openalex.org/W4293580221","https://openalex.org/W6966352619"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Deep":[0],"neural":[1,28,161],"networks":[2,29],"are":[3],"extremely":[4],"vulnerable":[5],"due":[6],"to":[7,18,25,50,67,81],"the":[8,20,23,31,38,52,76,82,92,96,111,132,135,140,143],"existence":[9,53],"of":[10,22,33,45,54,59,71,118,139],"adversarial":[11,34,46,55,72,77,97,119,146,165],"samples.":[12,35],"It":[13,151],"is":[14,48],"a":[15,60],"challenging":[16],"problem":[17],"optimize":[19],"robustness":[21],"model":[24,39,44,141],"protect":[26],"deep":[27,160],"from":[30],"threat":[32],"To":[36],"improve":[37],"robustness,":[40],"an":[41,153],"integrated":[42],"detection":[43,70,87,112,136],"samples":[47,73,78,98,120,147],"designed":[49],"detect":[51],"samples,":[56],"which":[57],"consists":[58],"multi-classification":[61],"detector":[62],"and":[63,74,108,110,134,155],"five":[64,101],"single-classification":[65],"detectors":[66],"perform":[68],"double-layer":[69],"intercept":[75],"finally":[79],"sent":[80],"image":[83],"classification":[84],"model.":[85],"The":[86],"experiments":[88,128],"were":[89,129],"conducted":[90,130],"on":[91,131],"CIFAR-10":[93],"dataset":[94],"for":[95,115,142,159],"generated":[99],"by":[100],"attack":[102,127,145],"algorithms:":[103],"FGSM,":[104],"BIM,":[105],"DeepFool,":[106],"JSMA,":[107],"C&W,":[109],"success":[113,137],"rate":[114,138],"all":[116],"types":[117],"reached":[121,148],"over":[122,149],"98.96%.":[123],"After":[124],"that,":[125],"secondary":[126],"model,":[133],"second":[144],"92.36%.":[150],"provides":[152],"efficient":[154],"robust":[156],"optimization":[157],"method":[158],"network":[162],"models":[163],"in":[164],"environments.":[166]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
