{"id":"https://openalex.org/W4399458754","doi":"https://doi.org/10.1145/3655755.3655782","title":"Self-examination Mechanism: Improving Standard Accuracy in Adversarial Purification","display_name":"Self-examination Mechanism: Improving Standard Accuracy in Adversarial Purification","publication_year":2024,"publication_date":"2024-03-14","ids":{"openalex":"https://openalex.org/W4399458754","doi":"https://doi.org/10.1145/3655755.3655782"},"language":"en","primary_location":{"id":"doi:10.1145/3655755.3655782","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3655755.3655782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 6th International Conference on Image Video and Signal Processing","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/A5000714917","display_name":"Sora Suegami","orcid":"https://orcid.org/0009-0008-7107-3676"},"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":"Sora Suegami","raw_affiliation_strings":["The University of Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0008-7107-3676","affiliations":[{"raw_affiliation_string":"The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092741852","display_name":"Yutaro Oguri","orcid":"https://orcid.org/0009-0003-9384-2994"},"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":"Yutaro Oguri","raw_affiliation_strings":["The University of Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0003-9384-2994","affiliations":[{"raw_affiliation_string":"The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061187625","display_name":"Zaiying Zhao","orcid":"https://orcid.org/0009-0009-8174-6798"},"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":"Zaiying Zhao","raw_affiliation_strings":["The University of Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0009-8174-6798","affiliations":[{"raw_affiliation_string":"The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099059019","display_name":"Yu Kagaya","orcid":"https://orcid.org/0009-0001-4036-0735"},"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":"Yu Kagaya","raw_affiliation_strings":["The University of Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0001-4036-0735","affiliations":[{"raw_affiliation_string":"The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048624196","display_name":"Toshihiko Yamasaki","orcid":"https://orcid.org/0000-0002-1784-2314"},"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":"Toshihiko Yamasaki","raw_affiliation_strings":["The University of Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0002-1784-2314","affiliations":[{"raw_affiliation_string":"The University of Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5000714917"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06328258,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"200","last_page":"206"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9779999852180481,"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"}},{"id":"https://openalex.org/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.9761000275611877,"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/adversarial-system","display_name":"Adversarial system","score":0.8751404285430908},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7424049377441406},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6826885938644409},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6664130091667175},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5733410716056824},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5521310567855835},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5243908762931824},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4920034110546112},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4849545955657959},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42626726627349854},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40756756067276}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8751404285430908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7424049377441406},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6826885938644409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6664130091667175},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5733410716056824},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5521310567855835},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5243908762931824},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4920034110546112},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4849545955657959},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42626726627349854},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40756756067276},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3655755.3655782","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3655755.3655782","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 6th International Conference on Image Video and Signal Processing","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":10,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2964137095","https://openalex.org/W2971626200","https://openalex.org/W2972262177","https://openalex.org/W2980260691","https://openalex.org/W3004298045","https://openalex.org/W3112955596","https://openalex.org/W4324080652","https://openalex.org/W6758377931","https://openalex.org/W6778150606"],"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/W2005234362","https://openalex.org/W2162970382","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"image":[2,49,84,167],"classification":[3,17,65,129,168,177],"models":[4],"are":[5],"vulnerable":[6],"to":[7,87],"adversarial":[8,20,92,120,149,180],"examples.":[9],"While":[10],"existing":[11],"defense":[12,39],"methods":[13],"have":[14],"improved":[15],"the":[16,47,60,64,82,94,97,101,105,108,111,117,128,141,145,183],"accuracy":[18,130,169,178],"for":[19,23,170,179,182],"examples,":[21],"that":[22],"clean":[24,132,166],"images":[25],"(i.e.,":[26,90],"without":[27,125],"perturbations)":[28],"often":[29],"decreases.":[30],"To":[31],"address":[32],"this":[33],"problem,":[34],"we":[35],"propose":[36],"a":[37,74],"new":[38],"mechanism":[40],"called":[41],"self-examination.":[42],"In":[43],"our":[44,153],"self-examination":[45],"mechanism,":[46],"input":[48,83],"is":[50,55,67,85],"first":[51],"classified":[52],"whether":[53],"it":[54,114],"attacked":[56,89],"or":[57],"not.":[58],"Then,":[59],"inference":[61],"process":[62],"of":[63,76,110,119,131],"model":[66,106],"verified":[68],"using":[69],"SHapley":[70],"Additive":[71],"exPlanations":[72],"(SHAP),":[73],"method":[75,95,143,154],"explainable":[77],"artificial":[78],"intelligence":[79],"(XAI).":[80],"If":[81],"determined":[86],"be":[88,123],"an":[91,174],"example),":[93],"outputs":[96,107],"result":[98,109],"revised":[99],"by":[100],"reclassification":[102,137],"model.":[103],"Otherwise,":[104],"classifier":[112],"as":[113],"is.":[115],"Thus,":[116],"misclassification":[118],"examples":[121,181],"can":[122],"prevented":[124],"significantly":[126],"reducing":[127],"images.":[133],"We":[134,151],"construct":[135],"two":[136],"models:":[138],"one":[139],"uses":[140,147],"XAI":[142],"and":[144,173],"other":[146],"diffusion-based":[148],"purification.":[150],"evaluated":[152],"on":[155,165],"WideResNet":[156],"trained":[157],"with":[158],"CIFAR-10.":[159],"Experimental":[160],"results":[161],"show":[162],"negligible":[163],"impact":[164],"both":[171],"reclassifiers":[172],"improvement":[175],"in":[176],"latter":[184],"reclassifier.":[185]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}