{"id":"https://openalex.org/W7147589679","doi":"https://doi.org/10.48550/arxiv.2603.28568","title":"XSPA: Crafting Imperceptible X-Shaped Sparse Adversarial Perturbations for Transferable Attacks on VLMs","display_name":"XSPA: Crafting Imperceptible X-Shaped Sparse Adversarial Perturbations for Transferable Attacks on VLMs","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7147589679","doi":"https://doi.org/10.48550/arxiv.2603.28568"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.28568","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28568","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.2603.28568","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017525809","display_name":"Chengyin Hu","orcid":"https://orcid.org/0009-0006-9589-0182"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Chengyin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102534764","display_name":"Jiaju Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Jiaju","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132725093","display_name":"Xuemeng Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Xuemeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109316415","display_name":"Qike Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qike","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132577872","display_name":"Yiwei Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Yiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104247821","display_name":"Ang Li","orcid":"https://orcid.org/0000-0001-7578-1911"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132672218","display_name":"Chunlei Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Chunlei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132637845","display_name":"Xiang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132650381","display_name":"Jiahuan Long","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long, Jiahuan","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.42570000886917114,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.42570000886917114,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.421099990606308,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.03579999879002571,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.54339998960495},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5076000094413757},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.4957999885082245},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.47929999232292175},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.35839998722076416},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.351500004529953},{"id":"https://openalex.org/keywords/epipolar-geometry","display_name":"Epipolar geometry","score":0.3458999991416931},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.3330000042915344},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32350000739097595}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6676999926567078},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.54339998960495},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5076000094413757},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.4957999885082245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4867999851703644},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.47929999232292175},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.35839998722076416},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.3458999991416931},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34459999203681946},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32580000162124634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.2996000051498413},{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2858000099658966},{"id":"https://openalex.org/C207363949","wikidata":"https://www.wikidata.org/wiki/Q462915","display_name":"Visual space","level":3,"score":0.2793000042438507},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.2732999920845032},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C118930307","wikidata":"https://www.wikidata.org/wiki/Q600590","display_name":"Tuple","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.28568","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28568","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.2603.28568","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28568","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":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7759155631065369}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-language":[0],"models":[1],"(VLMs)":[2],"rely":[3],"on":[4,146,181,201,208],"a":[5,36,116,124,138,252],"shared":[6,26,46],"visual-textual":[7],"representation":[8],"space":[9,27,48],"to":[10,74,100,218,226],"perform":[11],"tasks":[12],"such":[13],"as":[14,155,157],"zero-shot":[15],"classification,":[16],"image":[17,178],"captioning,":[18],"and":[19,49,63,78,121,144,149,162,205,221,236],"visual":[20,40,166],"question":[21],"answering":[22],"(VQA).":[23],"While":[24],"this":[25,84,132],"enables":[28],"strong":[29],"cross-task":[30,141,247],"generalization,":[31],"it":[32,67],"may":[33],"also":[34],"introduce":[35],"common":[37],"vulnerability:":[38],"small":[39],"perturbations":[41,99,108,239],"can":[42,244],"propagate":[43],"through":[44],"the":[45,169,182],"embedding":[47],"cause":[50],"correlated":[51],"semantic":[52,142,158],"failures":[53],"across":[54,191],"tasks.":[55,194],"This":[56],"risk":[57],"is":[58],"particularly":[59],"important":[60],"in":[61,160,249,256],"interactive":[62],"decision-support":[64],"settings,":[65],"yet":[66],"remains":[68],"unclear":[69],"whether":[70],"VLMs":[71],"are":[72],"robust":[73],"highly":[75,234],"constrained,":[76],"sparse,":[77],"geometrically":[79],"fixed":[80,241],"perturbations.":[81],"To":[82],"address":[83],"question,":[85],"we":[86],"propose":[87],"X-shaped":[88],"Sparse":[89],"Pixel":[90],"Attack":[91],"(XSPA),":[92],"an":[93],"imperceptible":[94],"structured":[95],"attack":[96,119],"that":[97,186,232],"restricts":[98],"two":[101],"intersecting":[102],"diagonal":[103],"lines.":[104],"Compared":[105],"with":[106,240],"dense":[107],"or":[109],"flexible":[110],"localized":[111],"patches,":[112],"XSPA":[113,135,172,187],"operates":[114],"under":[115],"much":[117],"stricter":[118],"budget":[120],"thus":[122],"provides":[123],"more":[125],"stringent":[126],"test":[127],"of":[128,177],"VLM":[129],"robustness.":[130],"Within":[131],"sparse":[133,235],"support,":[134],"jointly":[136],"optimizes":[137],"classification":[139],"objective,":[140],"guidance,":[143],"regularization":[145],"perturbation":[147],"magnitude":[148],"along-line":[150],"smoothness,":[151],"inducing":[152],"transferable":[153],"misclassification":[154],"well":[156],"drift":[159],"captioning":[161],"VQA":[163,222],"while":[164,211],"preserving":[165],"subtlety.":[167],"Under":[168],"default":[170],"setting,":[171],"modifies":[173],"only":[174],"about":[175],"1.76%":[176],"pixels.":[179],"Experiments":[180],"COCO":[183],"dataset":[184],"show":[185],"consistently":[188],"degrades":[189],"performance":[190],"all":[192],"three":[193],"Zero-shot":[195],"accuracy":[196],"drops":[197],"by":[198,216,224],"52.33":[199],"points":[200,207,220],"OpenAI":[202],"CLIP":[203],"ViT-L/14":[204],"67.00":[206],"OpenCLIP":[209],"ViT-B/16,":[210],"GPT-4-evaluated":[212],"caption":[213],"consistency":[214],"decreases":[215],"up":[217,225],"58.60":[219],"correctness":[223],"44.38":[227],"points.":[228],"These":[229],"results":[230],"suggest":[231],"even":[233],"visually":[237],"subtle":[238],"geometric":[242],"priors":[243],"substantially":[245],"disrupt":[246],"semantics":[248],"VLMs,":[250],"revealing":[251],"notable":[253],"robustness":[254],"gap":[255],"current":[257],"multimodal":[258],"systems.":[259]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}