{"id":"https://openalex.org/W7136202770","doi":"https://doi.org/10.48550/arxiv.2603.12796","title":"Spectral Defense Against Resource-Targeting Attack in 3D Gaussian Splatting","display_name":"Spectral Defense Against Resource-Targeting Attack in 3D Gaussian Splatting","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7136202770","doi":"https://doi.org/10.48550/arxiv.2603.12796"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12796","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12796","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.12796","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129625372","display_name":"Yang SC; Chen JY; Shang HF; Cheng TY; Tsou SC; Chen JR","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129448940","display_name":"Yi Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129619636","display_name":"Jiaming He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Jiaming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013973037","display_name":"Yueqi Duan","orcid":"https://orcid.org/0000-0002-1190-6663"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Yueqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129630951","display_name":"Zheng Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5113654955","display_name":"Yap-Peng Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Yap-Peng","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":0.42160001397132874,"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":0.42160001397132874,"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/T11019","display_name":"Image Enhancement Techniques","score":0.10849999636411667,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.07649999856948853,"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/gaussian","display_name":"Gaussian","score":0.7588000297546387},{"id":"https://openalex.org/keywords/gaussian-filter","display_name":"Gaussian filter","score":0.5303000211715698},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4652999937534332},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43810001015663147},{"id":"https://openalex.org/keywords/gaussian-network-model","display_name":"Gaussian network model","score":0.4074999988079071},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.37130001187324524},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3682999908924103}],"concepts":[{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.7588000297546387},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6919999718666077},{"id":"https://openalex.org/C65892221","wikidata":"https://www.wikidata.org/wiki/Q1113935","display_name":"Gaussian filter","level":3,"score":0.5303000211715698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5005000233650208},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43810001015663147},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42660000920295715},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4154999852180481},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.4074999988079071},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.37130001187324524},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3682999908924103},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C184050105","wikidata":"https://www.wikidata.org/wiki/Q273163","display_name":"Isotropy","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25859999656677246},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12796","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12796","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.12796","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12796","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.4057973027229309,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,102],"3D":[3,111],"Gaussian":[4,12,29,89,103],"Splatting":[5],"(3DGS)":[6],"deliver":[7],"high-quality":[8],"rendering,":[9],"yet":[10],"the":[11,19,60],"representation":[13],"exposes":[14],"a":[15,68,110,140],"new":[16],"attack":[17,23],"surface,":[18],"resource-targeting":[20],"attack.":[21],"This":[22],"poisons":[24],"training":[25,65],"images,":[26],"excessively":[27],"inducing":[28],"growth":[30],"to":[31,114,155,174,180,187],"cause":[32],"resource":[33],"exhaustion.":[34],"Although":[35],"efficiency-oriented":[36],"methods":[37],"such":[38],"as":[39,83],"smoothing,":[40],"thresholding,":[41],"and":[42,91,104,136,167,182],"pruning":[43],"have":[44],"been":[45],"explored,":[46],"these":[47],"spatial-domain":[48],"strategies":[49],"operate":[50],"on":[51,144],"visible":[52],"structures":[53],"but":[54],"overlook":[55],"how":[56],"stealthy":[57],"perturbations":[58],"distort":[59],"underlying":[61],"spectral":[62,142],"behaviors":[63],"of":[64],"data.":[66],"As":[67],"result,":[69],"poisoned":[70],"inputs":[71],"introduce":[72],"abnormal":[73],"high-frequency":[74,128],"amplifications":[75],"that":[76,161],"mislead":[77],"3DGS":[78],"into":[79],"interpreting":[80],"noisy":[81,157],"patterns":[82],"detailed":[84],"structures,":[85,129],"ultimately":[86],"causing":[87],"unstable":[88],"overgrowth":[90,171],"degraded":[92],"scene":[93],"fidelity.":[94],"To":[95],"address":[96],"this,":[97],"we":[98,137],"propose":[99],"\\textbf{Spectral":[100],"Defense}":[101],"image":[105],"fields.":[106],"We":[107],"first":[108],"design":[109],"frequency":[112],"filter":[113],"selectively":[115],"prune":[116],"Gaussians":[117],"exhibiting":[118],"abnormally":[119],"high":[120,132],"frequencies.":[121],"Since":[122],"natural":[123],"scenes":[124],"also":[125],"contain":[126],"legitimate":[127],"directly":[130],"suppressing":[131,170],"frequencies":[133,149],"is":[134],"insufficient,":[135],"further":[138],"develop":[139],"2D":[141],"regularization":[143],"renderings,":[145],"distinguishing":[146],"naturally":[147],"isotropic":[148],"while":[150],"penalizing":[151],"anisotropic":[152],"angular":[153],"energy":[154],"constrain":[156],"patterns.":[158],"Experiments":[159],"show":[160],"our":[162],"defense":[163],"builds":[164],"robust,":[165],"accurate,":[166],"secure":[168],"3DGS,":[169],"by":[172,178,185],"up":[173,179,186],"$5.92\\times$,":[175],"reducing":[176],"memory":[177],"$3.66\\times$,":[181],"improving":[183],"speed":[184],"$4.34\\times$":[188],"under":[189],"attacks.":[190]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-17T00:00:00"}
