{"id":"https://openalex.org/W3195269615","doi":"https://doi.org/10.1109/icip42928.2021.9506180","title":"Intelligent and Adaptive Mixup Technique for Adversarial Robustness","display_name":"Intelligent and Adaptive Mixup Technique for Adversarial Robustness","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3195269615","doi":"https://doi.org/10.1109/icip42928.2021.9506180","mag":"3195269615"},"language":"en","primary_location":{"id":"doi:10.1109/icip42928.2021.9506180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","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/A5024647585","display_name":"Akshay Agarwal","orcid":"https://orcid.org/0000-0001-7362-4752"},"institutions":[{"id":"https://openalex.org/I115441956","display_name":"Buffalo State University","ror":"https://ror.org/05ms04m92","country_code":"US","type":"education","lineage":["https://openalex.org/I115441956"]},{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Akshay Agarwal","raw_affiliation_strings":["SUNY Buffalo,NY,USA","SUNY Buffalo, NY, USA"],"affiliations":[{"raw_affiliation_string":"SUNY Buffalo,NY,USA","institution_ids":["https://openalex.org/I115441956","https://openalex.org/I63190737"]},{"raw_affiliation_string":"SUNY Buffalo, NY, USA","institution_ids":["https://openalex.org/I115441956","https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050521702","display_name":"Mayank Vatsa","orcid":"https://orcid.org/0000-0001-5952-2274"},"institutions":[{"id":"https://openalex.org/I154549908","display_name":"Indian Institute of Technology Jodhpur","ror":"https://ror.org/03yacj906","country_code":"IN","type":"education","lineage":["https://openalex.org/I154549908"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mayank Vatsa","raw_affiliation_strings":["IIT Jodhpur,India","IIT Jodhpur, India"],"affiliations":[{"raw_affiliation_string":"IIT Jodhpur,India","institution_ids":["https://openalex.org/I154549908"]},{"raw_affiliation_string":"IIT Jodhpur, India","institution_ids":["https://openalex.org/I154549908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011779957","display_name":"Richa Singh","orcid":"https://orcid.org/0000-0003-4060-4573"},"institutions":[{"id":"https://openalex.org/I154549908","display_name":"Indian Institute of Technology Jodhpur","ror":"https://ror.org/03yacj906","country_code":"IN","type":"education","lineage":["https://openalex.org/I154549908"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Richa Singh","raw_affiliation_strings":["IIT Jodhpur,India","IIT Jodhpur, India"],"affiliations":[{"raw_affiliation_string":"IIT Jodhpur,India","institution_ids":["https://openalex.org/I154549908"]},{"raw_affiliation_string":"IIT Jodhpur, India","institution_ids":["https://openalex.org/I154549908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003836761","display_name":"Nalini Ratha","orcid":"https://orcid.org/0000-0001-7913-5722"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]},{"id":"https://openalex.org/I115441956","display_name":"Buffalo State University","ror":"https://ror.org/05ms04m92","country_code":"US","type":"education","lineage":["https://openalex.org/I115441956"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nalini Ratha","raw_affiliation_strings":["SUNY Buffalo,NY,USA","SUNY Buffalo, NY, USA"],"affiliations":[{"raw_affiliation_string":"SUNY Buffalo,NY,USA","institution_ids":["https://openalex.org/I115441956","https://openalex.org/I63190737"]},{"raw_affiliation_string":"SUNY Buffalo, NY, USA","institution_ids":["https://openalex.org/I115441956","https://openalex.org/I63190737"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5024647585"],"corresponding_institution_ids":["https://openalex.org/I115441956","https://openalex.org/I63190737"],"apc_list":null,"apc_paid":null,"fwci":0.9518,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.8019331,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"824","last_page":"828"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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":1.0,"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":0.9886000156402588,"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":0.9585999846458435,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8118510246276855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8003330230712891},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6932397484779358},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6653578877449036},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6016232371330261},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5303664207458496},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47235023975372314},{"id":"https://openalex.org/keywords/memorization","display_name":"Memorization","score":0.45145803689956665},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44550174474716187},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4427480399608612},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42008280754089355},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32304757833480835},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3197929561138153},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07357192039489746}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8118510246276855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8003330230712891},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6932397484779358},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6653578877449036},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6016232371330261},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5303664207458496},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47235023975372314},{"id":"https://openalex.org/C30038468","wikidata":"https://www.wikidata.org/wiki/Q4354775","display_name":"Memorization","level":2,"score":0.45145803689956665},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44550174474716187},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4427480399608612},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42008280754089355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32304757833480835},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3197929561138153},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07357192039489746},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C145420912","wikidata":"https://www.wikidata.org/wiki/Q853077","display_name":"Mathematics education","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip42928.2021.9506180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","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":39,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1945616565","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2750384547","https://openalex.org/W2765407302","https://openalex.org/W2788741149","https://openalex.org/W2930926105","https://openalex.org/W2940483533","https://openalex.org/W2963207607","https://openalex.org/W2963857521","https://openalex.org/W3035584216","https://openalex.org/W3035790147","https://openalex.org/W3036069296","https://openalex.org/W3037702798","https://openalex.org/W3091059919","https://openalex.org/W3103245149","https://openalex.org/W3118608800","https://openalex.org/W3134853163","https://openalex.org/W3137695714","https://openalex.org/W3162587795","https://openalex.org/W3175493885","https://openalex.org/W4287553002","https://openalex.org/W4287691528","https://openalex.org/W4293846201","https://openalex.org/W4300511536","https://openalex.org/W4361805498","https://openalex.org/W6637373629","https://openalex.org/W6640425456","https://openalex.org/W6676297131","https://openalex.org/W6719080892","https://openalex.org/W6739868092","https://openalex.org/W6743688258","https://openalex.org/W6745136726","https://openalex.org/W6753209683","https://openalex.org/W6763485134","https://openalex.org/W6781669327","https://openalex.org/W6787335730"],"related_works":["https://openalex.org/W2397565061","https://openalex.org/W4387561393","https://openalex.org/W3163481960","https://openalex.org/W3093895509","https://openalex.org/W4377865163","https://openalex.org/W2795259429","https://openalex.org/W3193857078","https://openalex.org/W2888956734","https://openalex.org/W3208304128","https://openalex.org/W3000197790"],"abstract_inverted_index":{"Deep":[0],"neural":[1,108,160],"networks":[2,38,63,109,161],"are":[3],"generally":[4],"trained":[5],"using":[6],"large":[7],"amounts":[8],"of":[9,60,72,106,123,158],"data":[10,42,90,151],"to":[11,28,142,154,162],"achieve":[12],"state-of-the-art":[13],"accuracy":[14],"in":[15,86,114],"many":[16],"possible":[17],"computer":[18],"vision":[19],"and":[20,54],"image":[21,89],"analysis":[22],"applications":[23],"ranging":[24],"from":[25,47],"object":[26],"recognition":[27],"natural":[29],"language":[30],"processing.":[31],"It":[32,126],"is":[33,64,78,127,139],"also":[34],"claimed":[35],"that":[36],"these":[37,143],"can":[39,44],"memorize":[40],"the":[41,48,61,68,73,76,80,83,87,104,115,121,124,132,136,156],"which":[43],"be":[45],"extracted":[46],"network":[49,77,133],"parameters":[50,122],"such":[51,163],"as":[52],"weights":[53],"gradient":[55],"information.":[56],"The":[57],"adversarial":[58],"vulnerability":[59],"deep":[62,107,159],"usually":[65],"evaluated":[66],"on":[67,98,110],"unseen":[69],"test":[70],"set":[71],"databases.":[74],"If":[75],"memorizing":[79],"data,":[81],"then":[82],"small":[84,111,144],"perturbation":[85],"training":[88,116],"should":[91],"not":[92],"drastically":[93],"change":[94],"its":[95],"performance.":[96],"Based":[97],"this":[99],"assumption,":[100],"we":[101,147],"first":[102],"evaluate":[103],"robustness":[105,157],"perturbations":[112],"added":[113],"images":[117,137],"used":[118],"for":[119],"learning":[120],"network.":[125],"observed":[128],"that,":[129],"even":[130],"if":[131],"has":[134],"seen":[135],"it":[138],"still":[140],"vulnerable":[141],"perturbations.":[145,164],"Further,":[146],"propose":[148],"a":[149],"novel":[150],"augmentation":[152],"technique":[153],"increase":[155]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-14T23:14:49.485078","created_date":"2025-10-10T00:00:00"}