{"id":"https://openalex.org/W2899861195","doi":"https://doi.org/10.3390/make1010011","title":"An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning","display_name":"An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning","publication_year":2018,"publication_date":"2018-11-09","ids":{"openalex":"https://openalex.org/W2899861195","doi":"https://doi.org/10.3390/make1010011","mag":"2899861195"},"language":"en","primary_location":{"id":"doi:10.3390/make1010011","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make1010011","pdf_url":"https://www.mdpi.com/2504-4990/1/1/11/pdf?version=1545210178","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/1/1/11/pdf?version=1545210178","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018897861","display_name":"Adrien Chan-Hon-Tong","orcid":"https://orcid.org/0000-0002-7333-2765"},"institutions":[{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Adrien CHAN-HON-TONG","raw_affiliation_strings":["ONERA, Universit\u00e9 de Paris Saclay, F-91123 Palaiseau, France"],"raw_orcid":"https://orcid.org/0000-0002-7333-2765","affiliations":[{"raw_affiliation_string":"ONERA, Universit\u00e9 de Paris Saclay, F-91123 Palaiseau, France","institution_ids":["https://openalex.org/I277688954","https://openalex.org/I204730241"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5018897861"],"corresponding_institution_ids":["https://openalex.org/I204730241","https://openalex.org/I277688954"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.6552,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.77507042,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"1","issue":"1","first_page":"192","last_page":"204"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9939000010490417,"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":0.9785000085830688,"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":0.8898765444755554},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6578907370567322},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6555035710334778},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6376446485519409},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.632381021976471},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5765650272369385},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5698264837265015},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5197184085845947},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4986917972564697},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3474843502044678}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8898765444755554},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6578907370567322},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6555035710334778},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6376446485519409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.632381021976471},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5765650272369385},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5698264837265015},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5197184085845947},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4986917972564697},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3474843502044678},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make1010011","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make1010011","pdf_url":"https://www.mdpi.com/2504-4990/1/1/11/pdf?version=1545210178","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:HAL:hal-01960077v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01960077","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, 2018, 1, pp.192-204. ⟨10.3390/make1010011⟩","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.3390/make1010011","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make1010011","pdf_url":"https://www.mdpi.com/2504-4990/1/1/11/pdf?version=1545210178","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2118585731","https://openalex.org/W2145287260","https://openalex.org/W2154579312","https://openalex.org/W2163605009","https://openalex.org/W2180612164","https://openalex.org/W2340897893","https://openalex.org/W2345010043","https://openalex.org/W2396976214","https://openalex.org/W2543927648","https://openalex.org/W2604505099","https://openalex.org/W2742956140","https://openalex.org/W2744095836","https://openalex.org/W2745565856","https://openalex.org/W2770312844","https://openalex.org/W2790664081","https://openalex.org/W2805969103","https://openalex.org/W2913153485","https://openalex.org/W2919115771","https://openalex.org/W4200141802","https://openalex.org/W4243493583","https://openalex.org/W6677656871"],"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":{"Today,":[0],"the":[1,29,37,46,66],"main":[2,70],"two":[3],"security":[4],"issues":[5],"for":[6,88],"deep":[7,90],"learning":[8,21,91],"are":[9],"data":[10,50,82],"poisoning":[11,16,51,83],"and":[12],"adversarial":[13,33,63],"examples.":[14],"Data":[15],"consists":[17],"of":[18,28,45,72,79],"perverting":[19],"a":[20,25,76,89],"system":[22,38],"by":[23,61],"manipulating":[24],"small":[26],"subset":[27],"training":[30,67],"data,":[31],"while":[32],"examples":[34],"entail":[35],"bypassing":[36],"at":[39],"testing":[40,47],"time":[41],"with":[42],"low-amplitude":[43],"manipulation":[44],"sample.":[48],"Unfortunately,":[49],"that":[52],"is":[53],"invisible":[54,81],"to":[55,65,96],"human":[56],"eyes":[57],"can":[58],"be":[59],"generated":[60],"adding":[62],"noise":[64],"data.":[68],"The":[69],"contribution":[71],"this":[73],"paper":[74],"includes":[75],"successful":[77],"implementation":[78,94],"such":[80],"using":[84],"image":[85],"classification":[86,98],"datasets":[87],"pipeline.":[92],"This":[93],"leads":[95],"significant":[97],"accuracy":[99],"gaps.":[100]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}