{"id":"https://openalex.org/W4405900622","doi":"https://doi.org/10.48550/arxiv.2412.18815","title":"Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors","display_name":"Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors","publication_year":2024,"publication_date":"2024-12-25","ids":{"openalex":"https://openalex.org/W4405900622","doi":"https://doi.org/10.48550/arxiv.2412.18815"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2412.18815","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.18815","pdf_url":"https://arxiv.org/pdf/2412.18815","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2412.18815","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Phuc, Pham","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Phuc, Pham","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108468790","display_name":"Son T. Vuong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vuong, Son","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101809540","display_name":"Khang Nguyen","orcid":"https://orcid.org/0000-0003-3471-5533"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Khang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5108842480","display_name":"Tuan Dang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dang, Tuan","raw_affiliation_strings":[],"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":true,"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":0.9995999932289124,"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":0.9995999932289124,"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":0.9370999932289124,"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/bounding-overwatch","display_name":"Bounding overwatch","score":0.7754638195037842},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7541022300720215},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.6912310123443604},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.597054123878479},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5872315168380737},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5382385849952698},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.4910752773284912},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48450443148612976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4757508933544159},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.17612102627754211},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.11845088005065918}],"concepts":[{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.7754638195037842},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7541022300720215},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.6912310123443604},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.597054123878479},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5872315168380737},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5382385849952698},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4910752773284912},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48450443148612976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4757508933544159},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.17612102627754211},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.11845088005065918},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2412.18815","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.18815","pdf_url":"https://arxiv.org/pdf/2412.18815","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2412.18815","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2412.18815","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":"pmh:oai:arXiv.org:2412.18815","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2412.18815","pdf_url":"https://arxiv.org/pdf/2412.18815","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405900622.pdf","grobid_xml":"https://content.openalex.org/works/W4405900622.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4310988119","https://openalex.org/W4285226279","https://openalex.org/W4288019534"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"object":[2,49,61,90,153],"detection":[3],"has":[4],"become":[5],"ubiquitous":[6],"in":[7,16,28,98,104],"the":[8,36,45,64,105,125,132,137,144,156,185,198,230],"last":[9],"decade":[10],"due":[11],"to":[12,59,73,78,84,115,205,209],"its":[13],"high":[14],"accuracy":[15],"many":[17],"real-world":[18],"applications.":[19],"With":[20],"this":[21,52,225],"growing":[22],"trend,":[23],"these":[24],"models":[25,160],"are":[26,227],"interested":[27],"being":[29,38],"attacked":[30],"by":[31,88,135],"adversaries,":[32],"with":[33,128],"most":[34,157],"of":[35,47,66,139],"results":[37],"on":[39,120,174],"classifiers,":[40],"which":[41,95],"do":[42],"not":[43],"match":[44],"context":[46],"practical":[48],"detection.":[50],"In":[51],"work,":[53],"we":[54,109,147],"propose":[55],"a":[56,111],"novel":[57],"method":[58,82],"fool":[60],"detectors,":[62,68,154],"expose":[63],"vulnerability":[65],"state-of-the-art":[67,159],"and":[69,124,166,178,183,191,207,214,221],"promote":[70],"later":[71],"works":[72],"build":[74],"more":[75,112],"robust":[76],"detectors":[77],"adversarial":[79,86,149],"examples.":[80],"Our":[81,194],"aims":[83],"generate":[85],"images":[87],"perturbing":[89],"confidence":[91,100],"scores":[92],"during":[93],"training,":[94],"is":[96,203],"crucial":[97],"predicting":[99],"for":[101,224],"each":[102],"class":[103],"testing":[106],"phase.":[107],"Herein,":[108],"provide":[110],"intuitive":[113],"technique":[114,173],"embed":[116],"additive":[117],"noises":[118],"based":[119],"detected":[121],"objects'":[122],"masks":[123],"training":[126],"loss":[127],"distortion":[129],"control":[130],"over":[131],"original":[133],"image":[134,192],"leveraging":[136],"gradient":[138],"iterative":[140],"images.":[141],"To":[142],"verify":[143],"proposed":[145],"method,":[146],"perform":[148],"attacks":[150],"against":[151],"different":[152],"including":[155],"recent":[158],"like":[161],"YOLOv8,":[162],"Faster":[163],"R-CNN,":[164],"RetinaNet,":[165],"Swin":[167],"Transformer.":[168],"We":[169],"also":[170],"evaluate":[171],"our":[172],"MS":[175],"COCO":[176],"2017":[177],"PASCAL":[179],"VOC":[180],"2012":[181],"datasets":[182],"analyze":[184],"trade-off":[186],"between":[187],"success":[188,200],"attack":[189,201],"rate":[190,202],"distortion.":[193],"experiments":[195],"show":[196],"that":[197],"achievable":[199],"up":[204,208],"$100$\\%":[206],"$98$\\%":[210],"when":[211],"performing":[212],"white-box":[213],"black-box":[215],"attacks,":[216],"respectively.":[217],"The":[218],"source":[219],"code":[220],"relevant":[222],"documentation":[223],"work":[226],"available":[228],"at":[229],"following":[231],"link:":[232],"https://github.com/anonymous20210106/attack_detector":[233]},"counts_by_year":[],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2024-12-31T00:00:00"}