{"id":"https://openalex.org/W7160356420","doi":"https://doi.org/10.48550/arxiv.2605.01301","title":"From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception","display_name":"From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception","publication_year":2026,"publication_date":"2026-05-02","ids":{"openalex":"https://openalex.org/W7160356420","doi":"https://doi.org/10.48550/arxiv.2605.01301"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.01301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01301","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.01301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135343437","display_name":"Qingzhao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qingzhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135362480","display_name":"Runting Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Runting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135325803","display_name":"Z. Morley Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao, Z. Morley","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.8780999779701233,"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":0.8780999779701233,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.060100000351667404,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10917","display_name":"Smart Grid Security and Resilience","score":0.00800000037997961,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.6096000075340271},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4318000078201294},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.3686000108718872},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.36480000615119934},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3427000045776367},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.33250001072883606},{"id":"https://openalex.org/keywords/dangerous-driving","display_name":"Dangerous driving","score":0.322299987077713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6935999989509583},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6100999712944031},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.6096000075340271},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.42289999127388},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.36480000615119934},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3619999885559082},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C2779925283","wikidata":"https://www.wikidata.org/wiki/Q5216147","display_name":"Dangerous driving","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C2780741293","wikidata":"https://www.wikidata.org/wiki/Q4818019","display_name":"Attack patterns","level":3,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.01301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01301","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.01301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.01301","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.591445803642273}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Collaborative":[0],"perception":[1,10,110],"allows":[2],"connected":[3],"and":[4,129,139,158,167,181],"autonomous":[5],"vehicles":[6],"(CAVs)":[7],"to":[8,133,154,192,226],"improve":[9],"by":[11,34],"sharing":[12],"sensory":[13],"data,":[14,37],"but":[15],"it":[16],"also":[17,202],"introduces":[18],"security":[19],"risks":[20],"from":[21],"manipulated":[22],"inputs.":[23],"Prior":[24],"work":[25],"shows":[26],"that":[27,83,152,170,206],"attackers":[28],"can":[29,64],"spoof":[30],"or":[31,54],"remove":[32],"objects":[33,107],"fabricating":[35],"shared":[36,109],"yet":[38],"the":[39,103,171,221,229],"practicality":[40],"of":[41,93,105,194],"such":[42,185],"attacks":[43,50],"in":[44,56,68,108,136,177,190,211],"real-world":[45],"driving":[46,86,142],"remains":[47],"unclear.":[48],"Existing":[49],"are":[51,120],"often":[52],"detectable":[53,97],"evaluated":[55],"manually":[57],"constructed":[58],"scenarios,":[59,195],"leaving":[60],"open":[61],"whether":[62],"they":[63],"induce":[65],"safety-critical":[66,183,213],"outcomes":[67],"dynamic":[69,155],"environments.":[70],"To":[71],"bridge":[72],"this":[73],"gap,":[74],"we":[75],"present":[76],"a":[77,204],"stealthy,":[78],"scenario-realistic":[79],"data":[80],"fabrication":[81],"attack":[82,100,150,160,172],"induces":[84],"unsafe":[85,141],"behaviors":[87,138],"through":[88,123],"end-to-end":[89],"system":[90],"effects.":[91],"Instead":[92],"creating":[94],"large,":[95],"easily":[96],"anomalies,":[98],"our":[99],"subtly":[101],"manipulates":[102],"poses":[104],"existing":[106,231],"results,":[111],"keeping":[112],"perturbations":[113],"below":[114],"detection":[115,179,218],"thresholds.":[116],"These":[117],"small":[118,222],"errors":[119,180],"then":[121],"propagated":[122],"downstream":[124],"modules,":[125],"including":[126],"object":[127],"tracking":[128],"trajectory":[130],"prediction,":[131],"leading":[132],"significant":[134],"deviations":[135],"predicted":[137],"ultimately":[140],"decisions.":[143],"We":[144,201],"further":[145],"design":[146],"an":[147,216],"online,":[148],"scenario-aware":[149],"framework":[151],"adapts":[153],"traffic":[156],"conditions":[157],"optimizes":[159],"strategies":[161],"at":[162],"runtime.":[163],"Experiments":[164],"on":[165,208,220],"OPV2V":[166],"V2X-Real":[168],"demonstrate":[169],"achieves":[173],"over":[174],"90%":[175],"success":[176],"inducing":[178],"triggers":[182],"behaviors,":[184],"as":[186],"unnecessary":[187],"hard":[188],"braking,":[189],"up":[191],"50%":[193],"while":[196],"largely":[197],"evading":[198],"state-of-the-art":[199],"defenses.":[200],"propose":[203],"mitigation":[205],"focuses":[207],"detecting":[209],"anomalies":[210],"localized,":[212],"regions,":[214],"achieving":[215],"80%":[217],"rate":[219],"pose":[223],"perturbation":[224],"compared":[225],"11%":[227],"for":[228],"best":[230],"methods.":[232]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}