{"id":"https://openalex.org/W7156180220","doi":"https://doi.org/10.48550/arxiv.2604.22552","title":"Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models","display_name":"Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7156180220","doi":"https://doi.org/10.48550/arxiv.2604.22552"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22552","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22552","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":null,"license_id":null,"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.2604.22552","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134664296","display_name":"Shihui Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yan, Shihui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134726276","display_name":"Ziqi Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Ziqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134681967","display_name":"Yufei Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Yufei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134730142","display_name":"Yifan Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134685850","display_name":"Minghui Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Minghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134670312","display_name":"Shengshan Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Shengshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5134664296"],"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":0.9667999744415283,"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":0.9667999744415283,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.017100000753998756,"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"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.0017999999690800905,"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":0.7487000226974487},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6843000054359436},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.680899977684021},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.626800000667572},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.4731999933719635},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.46650001406669617},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.3959999978542328}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7487000226974487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7177000045776367},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6843000054359436},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.680899977684021},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.626800000667572},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.46650001406669617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4659000039100647},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3944000005722046},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C2776157020","wikidata":"https://www.wikidata.org/wiki/Q851598","display_name":"Physical security","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35589998960494995},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31360000371932983},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28929999470710754},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2879999876022339},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C42023084","wikidata":"https://www.wikidata.org/wiki/Q5249231","display_name":"Decision boundary","level":3,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22552","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22552","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.22552","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22552","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":null,"license_id":null,"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":{"Physical":[0],"adversarial":[1,73],"patch":[2,74],"attacks":[3,30,81],"critically":[4],"threaten":[5],"pedestrian":[6,72],"detection,":[7],"causing":[8],"surveillance":[9],"and":[10,17,52,104,143],"autonomous":[11],"driving":[12],"systems":[13],"to":[14,49,55,61,127,147,171],"miss":[15],"pedestrians":[16],"creating":[18],"severe":[19],"safety":[20],"risks.":[21],"Despite":[22],"their":[23],"effectiveness":[24],"in":[25,35],"controlled":[26],"settings,":[27],"existing":[28,172],"physical":[29,58,86,153],"face":[31],"two":[32],"major":[33],"limitations":[34],"practice:":[36],"they":[37,53],"lack":[38],"systematic":[39],"disruption":[40],"of":[41,97,115,132],"the":[42,116,129,133],"multi-stage":[43,79],"decision":[44],"pipeline,":[45],"enabling":[46],"residual":[47],"modules":[48],"offset":[50,102],"perturbations,":[51],"fail":[54],"model":[56],"complex":[57,152],"variations,":[59],"leading":[60],"poor":[62],"robustness.":[63],"To":[64],"overcome":[65],"these":[66],"limitations,":[67],"we":[68,91,121],"propose":[69],"a":[70,93,161],"novel":[71],"generation":[75],"method":[76],"that":[77,158],"combines":[78],"collaborative":[80],"with":[82],"robustness":[83,150],"enhancement":[84],"under":[85,139],"diversity,":[87],"called":[88],"TriPatch.":[89],"Specifically,":[90],"design":[92],"triplet":[94],"loss":[95,126],"consisting":[96],"detection":[98,117],"confidence":[99],"suppression,":[100],"bounding-box":[101],"amplification,":[103],"non-maximum":[105],"suppression":[106],"(NMS)":[107],"disruption,":[108],"which":[109],"jointly":[110],"act":[111],"across":[112,166],"different":[113],"stages":[114],"pipeline.":[118],"In":[119],"addition,":[120],"introduce":[122],"an":[123],"appearance":[124],"consistency":[125],"constrain":[128],"color":[130],"distribution":[131],"patch,":[134],"thereby":[135],"improving":[136],"its":[137],"adaptability":[138],"diverse":[140],"imaging":[141],"conditions,":[142],"incorporate":[144],"data":[145],"augmentation":[146],"further":[148],"enhance":[149],"against":[151],"perturbations.":[154],"Extensive":[155],"experiments":[156],"demonstrate":[157],"TriPatch":[159],"achieves":[160],"higher":[162],"attack":[163],"success":[164],"rate":[165],"multiple":[167],"detector":[168],"models":[169],"compared":[170],"approaches.":[173]},"counts_by_year":[],"updated_date":"2026-04-28T06:12:00.211691","created_date":"2026-04-28T00:00:00"}
