{"id":"https://openalex.org/W4401717565","doi":"https://doi.org/10.1109/access.2024.3446834","title":"Uncovering Distortion Differences: A Study of Adversarial Attacks and Machine Discriminability","display_name":"Uncovering Distortion Differences: A Study of Adversarial Attacks and Machine Discriminability","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401717565","doi":"https://doi.org/10.1109/access.2024.3446834"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3446834","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3446834","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3446834","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021716915","display_name":"Xiawei Wang","orcid":"https://orcid.org/0009-0007-8324-2070"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiawei Wang","raw_affiliation_strings":["Graduate Group in BioStatistics, University of California, Davis, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0009-0007-8324-2070","affiliations":[{"raw_affiliation_string":"Graduate Group in BioStatistics, University of California, Davis, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100411246","display_name":"Yao Li","orcid":"https://orcid.org/0000-0002-7195-5774"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Li","raw_affiliation_strings":["Department of Statistics and Operation Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA"],"raw_orcid":"https://orcid.org/0000-0002-7195-5774","affiliations":[{"raw_affiliation_string":"Department of Statistics and Operation Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003105932","display_name":"Cho-Jui Hsieh","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cho-Jui Hsieh","raw_affiliation_strings":["Department of Computer Science, University of California at Los Angeles, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of California at Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049635087","display_name":"Thomas C. M. Lee","orcid":"https://orcid.org/0000-0001-7067-405X"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas C. M. Lee","raw_affiliation_strings":["Department of Statistics, University of California at Davis, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-7067-405X","affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California at Davis, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2075,"currency":"USD","value_usd":2075},"apc_paid":{"value":2075,"currency":"USD","value_usd":2075},"fwci":0.2324,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.63974275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"12","issue":null,"first_page":"117872","last_page":"117883"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9700999855995178,"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":0.9700999855995178,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9047999978065491,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.777328610420227},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.7189834117889404},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.645138680934906},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5395551919937134},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44636696577072144},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.33010247349739075},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32539641857147217},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0917125940322876}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.777328610420227},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.7189834117889404},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.645138680934906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5395551919937134},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44636696577072144},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33010247349739075},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32539641857147217},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0917125940322876},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3446834","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3446834","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9741308dc8364e6db23f86a25f078893","is_oa":true,"landing_page_url":"https://doaj.org/article/9741308dc8364e6db23f86a25f078893","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 117872-117883 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3446834","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3446834","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.800000011920929,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G2045930519","display_name":"DMS-EPSRC Collaborative Research: Advancing Statistical Foundations and Frontiers for and from Emerging Astronomical Data Challenges","funder_award_id":"2113605","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2102708625","display_name":"FRG: Collaborative Research: Mathematical and Statistical Analysis of Compressible Data on Compressive Networks","funder_award_id":"2152289","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3069504102","display_name":"HDR TRIPODS: UC Davis TETRAPODS Institute of Data Science","funder_award_id":"1934568","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3812866752","display_name":"CAREER: Robustness Verification and Certified Defense for Machine Learning Models","funder_award_id":"2048280","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4133067109","display_name":"RTG: Networks: Foundations in Probability, Optimization, and Data Sciences","funder_award_id":"2134107","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5233990263","display_name":"RI: Small: Learning to Optimize: Designing and Improving Optimizers by Machine Learning Algorithms","funder_award_id":"2008173","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G645903391","display_name":"Collaborative Research: Emerging Variants of Generalized Fiducial Inference","funder_award_id":"2210388","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G703596469","display_name":"Collaborative Research: SLES: Verifying and Enforcing Safety Constraints in AI-based Sequential Generation","funder_award_id":"2331966","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1686810756","https://openalex.org/W1945616565","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2561975083","https://openalex.org/W2593892853","https://openalex.org/W2618043096","https://openalex.org/W2618530766","https://openalex.org/W2746600820","https://openalex.org/W2950468330","https://openalex.org/W2963070423","https://openalex.org/W2963612069","https://openalex.org/W2963857521","https://openalex.org/W2963920068","https://openalex.org/W2983044655","https://openalex.org/W3007384386","https://openalex.org/W3015625436","https://openalex.org/W3031351653","https://openalex.org/W3080260826","https://openalex.org/W3106412272","https://openalex.org/W3107235539","https://openalex.org/W3118608800","https://openalex.org/W3181412326","https://openalex.org/W3212212126","https://openalex.org/W4221160862","https://openalex.org/W4288103143","https://openalex.org/W4293584023","https://openalex.org/W4293846201","https://openalex.org/W4312518191","https://openalex.org/W4320150047","https://openalex.org/W4381325153","https://openalex.org/W6637162671","https://openalex.org/W6637373629","https://openalex.org/W6640425456","https://openalex.org/W6682262322","https://openalex.org/W6734483310","https://openalex.org/W6736640963","https://openalex.org/W6739868092","https://openalex.org/W6746402973","https://openalex.org/W6746608116","https://openalex.org/W6750404860","https://openalex.org/W6752654261","https://openalex.org/W6754108890","https://openalex.org/W6759129252","https://openalex.org/W6765597837","https://openalex.org/W6767666165","https://openalex.org/W6774469542","https://openalex.org/W6787972765","https://openalex.org/W6810426963"],"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":{"Deep":[0],"neural":[1,33],"networks":[2,34],"have":[3,16],"performed":[4],"remarkably":[5],"in":[6,89,144],"many":[7],"areas,":[8],"including":[9],"image-related":[10],"classification":[11],"tasks.":[12],"However,":[13],"various":[14,148],"studies":[15,54],"shown":[17],"that":[18,97],"they":[19],"are":[20],"vulnerable":[21],"to":[22,29,39,70,126],"adversarial":[23,50,75,98],"examples":[24],"\u2013":[25],"images":[26,99],"carefully":[27,123],"crafted":[28],"fool":[30],"well-trained":[31],"deep":[32],"by":[35,84],"introducing":[36],"imperceptible":[37],"perturbations":[38],"the":[40,46,55,79,85,115,118,128,142],"original":[41],"images.":[42],"To":[43,112],"better":[44],"understand":[45],"inherent":[47],"characteristics":[48],"of":[49,57,74,81,131],"attacks,":[51],"this":[52,120],"paper":[53,95,121],"features":[56],"three":[58],"common":[59],"attack":[60,102,149],"families:":[61],"gradient-based,":[62],"score-based,":[63],"and":[64,138],"decision-based.":[65],"The":[66],"primary":[67],"objective":[68],"is":[69],"recognize":[71],"distinct":[72],"types":[73,150],"examples,":[76],"as":[77],"identifying":[78],"type":[80],"information":[82],"possessed":[83],"attacker":[86],"can":[87,104],"aid":[88],"developing":[90],"effective":[91],"defense":[92],"strategies.":[93],"This":[94],"demonstrates":[96],"from":[100],"different":[101,132],"families":[103],"be":[105],"successfully":[106],"identified":[107],"with":[108],"a":[109],"simple":[110],"model.":[111],"further":[113],"investigate":[114],"reason":[116],"behind":[117],"observations,":[119],"conducts":[122],"designed":[124],"experiments":[125],"study":[127],"distortion":[129,145],"patterns":[130,146],"attacks.":[133],"Experimental":[134],"results":[135],"on":[136],"CIFAR10":[137],"Tiny":[139],"ImageNet":[140],"validated":[141],"differences":[143],"between":[147],"for":[151],"both$L_{2}$and$L_{\\infty":[152],"}":[153],"$norm.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
