{"id":"https://openalex.org/W7161289876","doi":"https://doi.org/10.48550/arxiv.2605.14868","title":"Fast Adversarial Attacks with Gradient Prediction","display_name":"Fast Adversarial Attacks with Gradient Prediction","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161289876","doi":"https://doi.org/10.48550/arxiv.2605.14868"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14868","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.14868","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043239539","display_name":"Kamil Ciosek","orcid":"https://orcid.org/0000-0002-0238-9393"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ciosek, Kamil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102807082","display_name":"Aleksandr Petrov","orcid":"https://orcid.org/0000-0001-5216-6588"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Petrov, Aleksandr V.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047511363","display_name":"Nicol\u00f2 Felicioni","orcid":"https://orcid.org/0000-0002-3555-7760"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Felicioni, Nicol\u00f2","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027774255","display_name":"Konstantina Palla","orcid":"https://orcid.org/0000-0001-7066-0539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Palla, Konstantina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9955000281333923,"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":0.9955000281333923,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.0005000000237487257,"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.00039999998989515007,"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":0.7752000093460083},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7152000069618225},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5730000138282776},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.5044000148773193},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.48500001430511475},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4311999976634979},{"id":"https://openalex.org/keywords/tangent","display_name":"Tangent","score":0.4027999937534332}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7752000093460083},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7152000069618225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5730000138282776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5116999745368958},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5116000175476074},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.48500001430511475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4763999879360199},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4311999976634979},{"id":"https://openalex.org/C138187205","wikidata":"https://www.wikidata.org/wiki/Q131251","display_name":"Tangent","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3952000141143799},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2777999937534332},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14868","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.14868","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14868","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generating":[0],"adversarial":[1,12,123],"examples":[2],"at":[3],"scale":[4],"is":[5,59,69],"a":[6,29,34,53,62,96,104,115],"core":[7],"primitive":[8],"for":[9,80],"robustness":[10],"evaluation,":[11],"training,":[13],"and":[14,68,117],"red-teaming,":[15],"yet":[16],"even":[17],"\"fast\"":[18],"attacks":[19,37],"such":[20],"as":[21,114],"FGSM":[22],"remain":[23],"throughput-limited":[24],"by":[25,43,61],"the":[26,40,45,72,100],"cost":[27],"of":[28,36,65,89,99],"backward":[30,41],"pass.":[31],"We":[32],"introduce":[33],"family":[35],"that":[38],"eliminates":[39],"pass":[42],"predicting":[44],"input":[46],"gradient":[47,112],"from":[48],"forward-pass":[49],"hidden":[50],"states":[51],"via":[52],"lightweight":[54],"linear":[55],"regression.":[56],"The":[57],"approach":[58],"motivated":[60],"kernel":[63],"view":[64],"neural":[66],"networks":[67],"exact":[70],"in":[71,107],"Neural":[73],"Tangent":[74],"Kernel":[75],"regime,":[76],"while":[77,93],"remaining":[78],"effective":[79],"practical":[81],"finite-width":[82],"models.":[83],"Empirically,":[84],"our":[85],"methods":[86],"recover":[87],"much":[88],"FGSM's":[90],"attack":[91],"performance":[92],"using":[94],"only":[95],"small":[97],"fraction":[98],"time,":[101],"corresponding":[102],"to":[103,120],"$532\\%$":[105],"increase":[106],"throughput.":[108],"These":[109],"results":[110],"suggest":[111],"prediction":[113],"simple":[116],"general":[118],"route":[119],"significantly":[121],"faster":[122],"generation":[124],"under":[125],"realistic":[126],"wall-clock":[127],"constraints.":[128]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-16T00:00:00"}
