{"id":"https://openalex.org/W2975486888","doi":"https://doi.org/10.1109/tsp.2019.2943232","title":"Perturbation Analysis of Learning Algorithms: Generation of Adversarial Examples From Classification to Regression","display_name":"Perturbation Analysis of Learning Algorithms: Generation of Adversarial Examples From Classification to Regression","publication_year":2019,"publication_date":"2019-09-23","ids":{"openalex":"https://openalex.org/W2975486888","doi":"https://doi.org/10.1109/tsp.2019.2943232","mag":"2975486888"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2019.2943232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2019.2943232","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Signal Processing","raw_type":"journal-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/A5023739887","display_name":"Emilio Rafael Balda","orcid":"https://orcid.org/0000-0002-9848-698X"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Emilio Rafael Balda","raw_affiliation_strings":["Institute for Theoretical Information Technology (TI), RWTH Aachen University, Aachen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-9848-698X","affiliations":[{"raw_affiliation_string":"Institute for Theoretical Information Technology (TI), RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083706779","display_name":"Arash Behboodi","orcid":"https://orcid.org/0000-0001-8229-2809"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Arash Behboodi","raw_affiliation_strings":["Institute for Theoretical Information Technology (TI), RWTH Aachen University, Aachen, Germany"],"raw_orcid":"https://orcid.org/0000-0001-8229-2809","affiliations":[{"raw_affiliation_string":"Institute for Theoretical Information Technology (TI), RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066277118","display_name":"Rudolf Mathar","orcid":"https://orcid.org/0000-0002-9585-605X"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Rudolf Mathar","raw_affiliation_strings":["Institute for Theoretical Information Technology (TI), RWTH Aachen University, Aachen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-9585-605X","affiliations":[{"raw_affiliation_string":"Institute for Theoretical Information Technology (TI), RWTH Aachen University, Aachen, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023739887"],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":2.6126,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.92235116,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"67","issue":"23","first_page":"6078","last_page":"6091"},"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/T14117","display_name":"Integrated Circuits and Semiconductor Failure Analysis","score":0.983299970626831,"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"}},{"id":"https://openalex.org/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.963699996471405,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.8235491514205933},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7146368026733398},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6190211176872253},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5595857501029968},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5591950416564941},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.545815110206604},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.512561559677124},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.4841180741786957},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.41336822509765625},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3906794488430023},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19070649147033691},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0781870186328888}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8235491514205933},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7146368026733398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6190211176872253},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5595857501029968},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5591950416564941},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.545815110206604},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.512561559677124},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.4841180741786957},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.41336822509765625},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3906794488430023},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19070649147033691},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0781870186328888},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2019.2943232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2019.2943232","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W143004564","https://openalex.org/W639708223","https://openalex.org/W1673923490","https://openalex.org/W1928278792","https://openalex.org/W1945616565","https://openalex.org/W1985123706","https://openalex.org/W2032711963","https://openalex.org/W2097117768","https://openalex.org/W2125908420","https://openalex.org/W2160815625","https://openalex.org/W2180612164","https://openalex.org/W2194775991","https://openalex.org/W2208167373","https://openalex.org/W2232850827","https://openalex.org/W2243397390","https://openalex.org/W2418949411","https://openalex.org/W2508156266","https://openalex.org/W2543927648","https://openalex.org/W2559944883","https://openalex.org/W2594717275","https://openalex.org/W2604147826","https://openalex.org/W2604505099","https://openalex.org/W2610918198","https://openalex.org/W2618235498","https://openalex.org/W2618530766","https://openalex.org/W2727966874","https://openalex.org/W2765424254","https://openalex.org/W2767471303","https://openalex.org/W2773530077","https://openalex.org/W2797455600","https://openalex.org/W2941205169","https://openalex.org/W2962700793","https://openalex.org/W2963037989","https://openalex.org/W2963118571","https://openalex.org/W2963446712","https://openalex.org/W2963834268","https://openalex.org/W2963857521","https://openalex.org/W2964043206","https://openalex.org/W2964082701","https://openalex.org/W2964116600","https://openalex.org/W3103557498","https://openalex.org/W3118608800","https://openalex.org/W4295803779","https://openalex.org/W4300434632","https://openalex.org/W4300511536","https://openalex.org/W6637162671","https://openalex.org/W6638444622","https://openalex.org/W6640425456","https://openalex.org/W6688466223","https://openalex.org/W6689964813","https://openalex.org/W6716796288","https://openalex.org/W6719080892","https://openalex.org/W6725508424","https://openalex.org/W6725672702","https://openalex.org/W6730413380","https://openalex.org/W6736452954","https://openalex.org/W6736828813","https://openalex.org/W6738369334","https://openalex.org/W6740446203","https://openalex.org/W6741831866","https://openalex.org/W6746262365","https://openalex.org/W6750463028","https://openalex.org/W6755310938","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W4246396837","https://openalex.org/W2482350142","https://openalex.org/W3176240006","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4288019534","https://openalex.org/W4310988119"],"abstract_inverted_index":{"Despite":[0],"the":[1,35,55,66,125,133,173],"tremendous":[2],"success":[3],"of":[4,24,58,128,175],"deep":[5],"neural":[6],"networks":[7],"in":[8,34,99],"various":[9,93,118,141],"learning":[10,59,85],"problems,":[11],"it":[12],"has":[13],"been":[14,124],"observed":[15],"that":[16,42,52,166],"adding":[17],"intentionally":[18],"designed":[19],"adversarial":[20,43,72,129,138,167],"perturbations":[21,139,168],"to":[22,28,80,114,136,156],"inputs":[23],"these":[25,152],"architectures":[26],"leads":[27],"erroneous":[29],"classification":[30,88,109,121],"with":[31,96],"high":[32],"confidence":[33],"prediction.":[36],"In":[37,102],"this":[38],"work,":[39],"we":[40,104,131],"show":[41],"examples":[44],"can":[45,169],"be":[46,115],"generated":[47],"using":[48],"a":[49,63],"generic":[50],"approach":[51,68,135],"relies":[53],"on":[54,117],"perturbation":[56],"analysis":[57],"algorithms.":[60],"Formulated":[61],"as":[62,74],"convex":[64],"program,":[65],"proposed":[67,134],"retrieves":[69],"many":[70,100],"current":[71],"attacks":[73,83,107,153],"special":[75],"cases.":[76],"It":[77],"is":[78],"used":[79],"propose":[81],"novel":[82],"against":[84,108],"algorithms":[86,110],"for":[87,140,145],"and":[89,148,160],"regression":[90,142,176],"tasks":[91,122,177],"under":[92],"new":[94,106],"constraints":[95],"closed-form":[97],"solutions":[98],"instances.":[101],"particular,":[103],"derive":[105],"which":[111],"are":[112,154],"shown":[113],"top-performing":[116],"architectures.":[119],"Although":[120],"have":[123],"main":[126],"focus":[127],"attacks,":[130,151],"use":[132],"generate":[137],"tasks.":[143],"Designed":[144],"single":[146,149],"pixel":[147],"subset":[150],"applied":[155],"autoencoding,":[157],"image":[158],"colorization":[159],"real-time":[161],"object":[162],"detection":[163],"tasks,":[164],"showing":[165],"degrade":[170],"equally":[171],"gravely":[172],"output":[174],"<sup":[178],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[179],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[180],".":[181]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":2}],"updated_date":"2026-05-15T08:27:34.491423","created_date":"2025-10-10T00:00:00"}
