{"id":"https://openalex.org/W4224312366","doi":"https://doi.org/10.1145/3510548.3519373","title":"Enhancing Boundary Attack in Adversarial Image Using Square Random Constraint","display_name":"Enhancing Boundary Attack in Adversarial Image Using Square Random Constraint","publication_year":2022,"publication_date":"2022-04-18","ids":{"openalex":"https://openalex.org/W4224312366","doi":"https://doi.org/10.1145/3510548.3519373"},"language":"en","primary_location":{"id":"doi:10.1145/3510548.3519373","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3510548.3519373","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 ACM on International Workshop on Security and Privacy Analytics","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/A5030473503","display_name":"Tran Van Sang","orcid":"https://orcid.org/0000-0002-4211-049X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tran Van Sang","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100827222","display_name":"Tr\u1ea7n Ph\u01b0\u01a1ng Th\u1ea3o","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tran Phuong Thao","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050572143","display_name":"Rie Shigetomi Yamaguchi","orcid":"https://orcid.org/0000-0002-6359-2221"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Rie Shigetomi Yamaguchi","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088058883","display_name":"Toshiyuki Nakata","orcid":"https://orcid.org/0000-0001-6383-7105"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshiyuki Nakata","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030473503"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.2652,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.59139924,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"13","last_page":"23"},"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/T11515","display_name":"Bacillus and Francisella bacterial research","score":0.9696000218391418,"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"}},{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9614999890327454,"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/computer-science","display_name":"Computer science","score":0.6932113766670227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5607746839523315},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.552707314491272},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4937323033809662},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45468899607658386},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4149358868598938},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36253821849823},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3532091975212097},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3256074786186218},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2052624523639679}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6932113766670227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5607746839523315},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.552707314491272},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4937323033809662},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45468899607658386},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4149358868598938},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36253821849823},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3532091975212097},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3256074786186218},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2052624523639679},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3510548.3519373","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3510548.3519373","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 ACM on International Workshop on Security and Privacy Analytics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2103868202","https://openalex.org/W2243397390","https://openalex.org/W2560674852","https://openalex.org/W2745565856","https://openalex.org/W2746600820","https://openalex.org/W2861867928","https://openalex.org/W2890324916","https://openalex.org/W2890991187","https://openalex.org/W2895097814","https://openalex.org/W2906208681","https://openalex.org/W2963165363","https://openalex.org/W2963542245","https://openalex.org/W2963857521","https://openalex.org/W2964082701","https://openalex.org/W2964205597","https://openalex.org/W2977099891","https://openalex.org/W3088909400","https://openalex.org/W3106412272","https://openalex.org/W3107235539","https://openalex.org/W4252559882"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W4285226279","https://openalex.org/W4380075502"],"abstract_inverted_index":{"An":[0],"adversarial":[1,25,59,66,71,257],"image":[2,18,22,126,210,221,236,258,262],"is":[3,52,78],"a":[4,101,120,178],"sample":[5],"with":[6,228,232,265],"intentional":[7],"small":[8],"perturbations":[9],"that":[10,218,246],"causes":[11,134],"deep":[12,49],"learning":[13,50],"models":[14,51,212],"to":[15,54,86,128,158,189],"classify":[16],"the":[17,21,81,106,112,115,124,130,141,145,149,163,167,174,182,186,190,206,215,220,233,239,247,253,256,260,266],"incorrectly.":[19],"In":[20,166],"recognition":[23,211],"field,":[24],"images":[26,60],"have":[27],"become":[28],"an":[29,160],"attractive":[30],"research":[31],"topic":[32],"because":[33],"they":[34],"can":[35,204],"efficiently":[36],"attack":[37,83],"many":[38],"state-of-the-art":[39,82],"and":[40,61,92,259],"even":[41],"commercial":[42],"models.":[43],"The":[44],"challenge":[45],"now":[46],"for":[47,114,162],"any":[48],"how":[53],"find":[55],"out":[56],"potentially":[57],"sophisticated":[58],"prepare":[62],"proactive":[63],"prevention":[64],"against":[65],"attacks.":[67],"Among":[68],"various":[69,136],"existing":[70,104,191],"attacks,":[72],"Boundary":[73,107,164],"Attack,":[74],"proposed":[75,230,248],"in":[76,95,105,153,177,181,196,199,223],"2018,":[77],"one":[79],"of":[80,148,185,208,269],"methods":[84],"due":[85],"its":[87],"efficiency,":[88],"extreme":[89],"flexibility,":[90],"simplicity,":[91],"high":[93,146],"utilization":[94],"real-world":[96],"applications.":[97],"However,":[98],"we":[99,156,172],"found":[100],"severe":[102],"drawback":[103],"Attack.":[108,165],"First,":[109],"when":[110],"randomizing":[111],"direction":[113,169,176],"next":[116,131],"perturbation,":[117],"it":[118],"uses":[119],"Gaussian":[121],"distribution":[122],"over":[123],"entire":[125],"space":[127],"choose":[129],"movement.":[132],"This":[133],"losing":[135],"useful":[137],"statistic":[138],"information":[139],"from":[140,214],"models,":[142],"such":[143],"as":[144,194],"usage":[147],"convolutional":[150,216],"layers.":[151],"Therefore,":[152],"this":[154],"paper,":[155],"aim":[157],"investigate":[159],"enhancement":[161],"perturbation":[168,175],"randomization":[170,192],"step,":[171],"restrict":[173],"square":[179,224],"shape":[180],"geometrical":[183],"presentation":[184],"image.":[187],"Compared":[188],"strategy,":[193],"described":[195],"more":[197],"detail":[198],"Section":[200],"1.2,":[201],"our":[202,229],"approach":[203],"exploit":[205],"nature":[207],"most":[209],"originating":[213],"layers":[217],"capture":[219],"features":[222],"patterns.":[225],"We":[226],"experimented":[227],"method":[231,249],"well-known":[234],"CIFAR-10":[235],"dataset":[237],"on":[238],"ResNet-v2":[240],"model.":[241],"Our":[242],"experimental":[243],"result":[244],"showed":[245],"could":[250],"successfully":[251],"reduce":[252],"similarity":[254],"between":[255],"original":[261],"by":[263],"41.06%":[264],"same":[267],"number":[268],"queries.":[270]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}