{"id":"https://openalex.org/W4388189662","doi":"https://doi.org/10.1145/3581783.3612598","title":"Simple Techniques are Sufficient for Boosting Adversarial Transferability","display_name":"Simple Techniques are Sufficient for Boosting Adversarial Transferability","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4388189662","doi":"https://doi.org/10.1145/3581783.3612598"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612598","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5057230698","display_name":"Chaoning Zhang","orcid":"https://orcid.org/0000-0001-6007-6099"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chaoning Zhang","raw_affiliation_strings":["Kyung Hee University, Yongin, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0001-6007-6099","affiliations":[{"raw_affiliation_string":"Kyung Hee University, Yongin, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000590564","display_name":"Philipp Benz","orcid":"https://orcid.org/0000-0002-4389-8282"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Philipp Benz","raw_affiliation_strings":["Deeping Source, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-4389-8282","affiliations":[{"raw_affiliation_string":"Deeping Source, Seoul, Republic of Korea","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024432805","display_name":"Adil Karjauv","orcid":"https://orcid.org/0009-0002-4639-3076"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Adil Karjauv","raw_affiliation_strings":["KAIST, Daejeon, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0002-4639-3076","affiliations":[{"raw_affiliation_string":"KAIST, Daejeon, Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012455275","display_name":"In So Kweon","orcid":"https://orcid.org/0000-0001-9626-5983"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"In So Kweon","raw_affiliation_strings":["KAIST, Daejeon, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0001-9626-5983","affiliations":[{"raw_affiliation_string":"KAIST, Daejeon, Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034052371","display_name":"Choong Seon Hong","orcid":"https://orcid.org/0000-0003-3484-7333"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Choong Seon Hong","raw_affiliation_strings":["Kyung Hee University, Yongin, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-3484-7333","affiliations":[{"raw_affiliation_string":"Kyung Hee University, Yongin, Republic of Korea","institution_ids":["https://openalex.org/I35928602"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8486","last_page":"8494"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9858999848365784,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9358999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6989966630935669},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6107972264289856},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.6014712452888489},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.54893559217453},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5053442120552063},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4773435890674591},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47471606731414795},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.47465115785598755},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4450487494468689},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.42283952236175537},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3835403025150299},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3545164465904236},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34599125385284424}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6989966630935669},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6107972264289856},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.6014712452888489},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.54893559217453},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5053442120552063},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4773435890674591},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47471606731414795},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.47465115785598755},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4450487494468689},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.42283952236175537},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3835403025150299},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3545164465904236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34599125385284424},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612598","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8353039658","display_name":null,"funder_award_id":"KHU-20222221","funder_id":"https://openalex.org/F4320321332","funder_display_name":"Kyung Hee University"}],"funders":[{"id":"https://openalex.org/F4320321332","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2603766943","https://openalex.org/W2745565856","https://openalex.org/W2746600820","https://openalex.org/W2774644650","https://openalex.org/W2896078964","https://openalex.org/W2963857521","https://openalex.org/W2969542116","https://openalex.org/W2972986629","https://openalex.org/W2984699060","https://openalex.org/W2998421476","https://openalex.org/W3034643863","https://openalex.org/W3106412272","https://openalex.org/W3106835084","https://openalex.org/W4304701424","https://openalex.org/W4312563846","https://openalex.org/W6600503824"],"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/W4288019534"],"abstract_inverted_index":{"Transferable":[0],"targeted":[1],"adversarial":[2],"attack":[3,33,42,87],"against":[4],"deep":[5],"image":[6],"classifiers":[7,62],"has":[8],"remained":[9],"an":[10],"open":[11],"issue.":[12],"Depending":[13],"on":[14],"the":[15,19,21,53,57,70,74,82,111,132,136,143,150],"space":[16,32,37,41,45,86],"to":[17,109,149],"optimize":[18],"loss,":[20],"existing":[22],"methods":[23],"can":[24],"be":[25],"divided":[26],"into":[27],"two":[28,92,127],"categories:":[29],"(a)":[30],"feature":[31,40,115],"and":[34,88,142],"(b)":[35],"output":[36,44,85],"attack.":[38],"The":[39],"outperforms":[43],"one":[46,99,129],"by":[47],"a":[48,122],"large":[49],"margin":[50],"but":[51],"at":[52],"cost":[54],"of":[55,59,84],"requiring":[56],"training":[58],"layer-wise":[60],"auxiliary":[61],"for":[63,73,105,130,145,161],"each":[64],"corresponding":[65],"target":[66,151],"class":[67],"together":[68],"with":[69,117,126],"greedy":[71],"search":[72],"optimal":[75],"layers.":[76],"In":[77],"this":[78],"work,":[79],"we":[80,95,107,120],"revisit":[81],"method":[83],"improve":[89],"it":[90,147],"from":[91,135],"perspectives.":[93],"First,":[94],"identify":[96],"over-fitting":[97],"as":[98],"major":[100],"factor":[101],"that":[102,155],"hinders":[103],"transferability,":[104],"which":[106],"propose":[108,121],"augment":[110],"network":[112],"input":[113],"and/or":[114],"layers":[116],"noise.":[118],"Second,":[119],"new":[123],"cross-entropy":[124],"loss":[125],"ends:":[128],"pushing":[131],"sample":[133],"far":[134],"source":[137],"class,":[138,141],"i.e.":[139],"ground-truth":[140],"other":[144],"pulling":[146],"close":[148],"class.":[152],"We":[153],"demonstrate":[154],"simple":[156],"techniques":[157],"are":[158],"sufficient":[159],"enough":[160],"achieving":[162],"very":[163],"competitive":[164],"performance.":[165]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}