{"id":"https://openalex.org/W2963571290","doi":"https://doi.org/10.1109/vizsec.2017.8062202","title":"Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning","display_name":"Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2963571290","doi":"https://doi.org/10.1109/vizsec.2017.8062202","mag":"2963571290"},"language":"en","primary_location":{"id":"doi:10.1109/vizsec.2017.8062202","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vizsec.2017.8062202","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium on Visualization for Cyber Security (VizSec)","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/A5103860963","display_name":"Andrew P. Norton","orcid":null},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew P. Norton","raw_affiliation_strings":["Department of Computer Science, University of Virginia"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Virginia","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101887931","display_name":"Yanjun Qi","orcid":"https://orcid.org/0000-0002-5796-7453"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjun Qi","raw_affiliation_strings":["University of Virginia, Charlottesville, VA, US"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, VA, US","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103860963"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":3.7376,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.9474606,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9945999979972839,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8313347697257996},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7998661398887634},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7829185128211975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.696426510810852},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.6234527826309204},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5941146016120911},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5909834504127502},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.5282955169677734},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.5007650852203369},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4519854784011841}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8313347697257996},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7998661398887634},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7829185128211975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.696426510810852},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.6234527826309204},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5941146016120911},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5909834504127502},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.5282955169677734},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.5007650852203369},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4519854784011841},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vizsec.2017.8062202","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vizsec.2017.8062202","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium on Visualization for Cyber Security (VizSec)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6000000238418579,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1825675169","https://openalex.org/W1945616565","https://openalex.org/W2112796928","https://openalex.org/W2125908420","https://openalex.org/W2163605009","https://openalex.org/W2180612164","https://openalex.org/W2325939864","https://openalex.org/W2516574342","https://openalex.org/W2528914598","https://openalex.org/W2963207607","https://openalex.org/W2963857521","https://openalex.org/W2964153729","https://openalex.org/W6637162671","https://openalex.org/W6638389677","https://openalex.org/W6684191040","https://openalex.org/W6685736903","https://openalex.org/W6700903540","https://openalex.org/W6726114608","https://openalex.org/W6728004082"],"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":{"Recent":[0],"studies":[1],"have":[2],"shown":[3],"that":[4,193],"attackers":[5],"can":[6,71,121,129,181],"force":[7],"deep":[8,31,61,69,137],"learning":[9,32,46,50,138],"models":[10,70],"to":[11,24,47,56,66,85,110,115,154,187],"misclassify":[12],"so-called":[13],"\u201cadversarial":[14],"examples:\u201d":[15],"maliciously":[16],"generated":[17],"images":[18,171],"formed":[19],"by":[20,74,202],"making":[21],"imperceptible":[22],"modifications":[23],"pixel":[25],"values.":[26],"With":[27],"growing":[28],"interest":[29],"in":[30,150,167],"for":[33,39],"security":[34,40,131],"applications,":[35],"it":[36,63],"is":[37,64,101,148,165],"important":[38],"experts":[41,132],"and":[42,104,114,169,184,227],"users":[43],"of":[44,60,89,136,159,205,216],"machine":[45],"recognize":[48],"how":[49,68],"systems":[51],"may":[52],"be":[53,72],"attacked.":[54],"Due":[55],"the":[57,87,155,199],"complex":[58],"nature":[59],"learning,":[62],"challenging":[65,149],"understand":[67,116],"fooled":[73],"adversarial":[75,91,119,163],"examples.":[76],"Thus,":[77],"we":[78,191],"present":[79],"a":[80,94,123,140,213,230],"web-based":[81],"visualization":[82,147],"tool,":[83],"Adversarial-Playground,":[84],"demonstrate":[86],"efficacy":[88],"common":[90],"methods":[92],"against":[93],"convolutional":[95],"neural":[96],"network":[97],"(CNN)":[98],"system.":[99],"Adversarial-Playground":[100],"educational,":[102],"modular":[103],"interactive.":[105],"(1)":[106],"It":[107,128],"enables":[108],"non-experts":[109],"compare":[111],"examples":[112,164],"visually":[113],"why":[117],"an":[118,145,203],"example":[120],"fool":[122],"CNN-based":[124],"image":[125,160],"classifier.":[126],"(2)":[127],"help":[130],"explore":[133],"more":[134],"vulnerability":[135],"as":[139,223,225],"software":[141],"module.":[142],"(3)":[143],"Building":[144],"interactive":[146],"this":[151],"domain":[152],"due":[153],"large":[156],"feature":[157],"space":[158],"classification":[161],"(generating":[162],"slow":[166],"general":[168],"visualizing":[170],"are":[172],"costly).":[173],"Through":[174],"multiple":[175],"novel":[176],"design":[177],"choices,":[178],"our":[179,194],"tool":[180],"provide":[182],"fast":[183,224],"accurate":[185],"responses":[186],"user":[188],"requests.":[189],"Empirically,":[190],"find":[192],"client-server":[195],"division":[196],"strategy":[197],"reduced":[198],"response":[200],"time":[201],"average":[204],"1.5":[206],"seconds":[207],"per":[208],"sample.":[209],"Our":[210],"other":[211],"innovation,":[212],"faster":[214],"variant":[215],"JSMA":[217,226],"evasion":[218,232],"algorithm,":[219],"empirically":[220],"performed":[221],"twice":[222],"yet":[228],"maintains":[229],"comparable":[231],"rate":[233],"<sup":[234],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[235],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[236],".":[237]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":2}],"updated_date":"2026-04-02T13:48:15.688549","created_date":"2025-10-10T00:00:00"}