{"id":"https://openalex.org/W4417025093","doi":"https://doi.org/10.48550/arxiv.2512.03121","title":"Lost in Modality: Evaluating the Effectiveness of Text-Based Membership Inference Attacks on Large Multimodal Models","display_name":"Lost in Modality: Evaluating the Effectiveness of Text-Based Membership Inference Attacks on Large Multimodal Models","publication_year":2025,"publication_date":"2025-12-02","ids":{"openalex":"https://openalex.org/W4417025093","doi":"https://doi.org/10.48550/arxiv.2512.03121"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.03121","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03121","pdf_url":"https://arxiv.org/pdf/2512.03121","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.03121","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045823167","display_name":"Ziyi Tong","orcid":"https://orcid.org/0009-0007-5701-9705"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tong, Ziyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100563417","display_name":"Feifei Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Feifei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077641909","display_name":"Le-Minh Nguyen","orcid":"https://orcid.org/0000-0002-2265-1010"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Le Minh","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045823167"],"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":0.53329998254776,"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":0.53329998254776,"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/T10028","display_name":"Topic Modeling","score":0.13670000433921814,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06849999725818634,"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/inference","display_name":"Inference","score":0.6111000180244446},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.4415999948978424},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3953999876976013},{"id":"https://openalex.org/keywords/multimodality","display_name":"Multimodality","score":0.35109999775886536},{"id":"https://openalex.org/keywords/language-understanding","display_name":"Language understanding","score":0.3156999945640564}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7113999724388123},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6111000180244446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5015000104904175},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.4415999948978424},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43810001015663147},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2540000081062317}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.03121","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03121","pdf_url":"https://arxiv.org/pdf/2512.03121","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.03121","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.03121","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:2512.03121","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.03121","pdf_url":"https://arxiv.org/pdf/2512.03121","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Large":[0],"Multimodal":[1],"Language":[2],"Models":[3],"(MLLMs)":[4],"are":[5],"emerging":[6],"as":[7,110],"one":[8],"of":[9,17,62],"the":[10,58,81],"foundational":[11],"tools":[12],"in":[13,23,44,52,89,104],"an":[14],"expanding":[15],"range":[16],"applications.":[18],"Consequently,":[19],"understanding":[20],"training-data":[21],"leakage":[22],"these":[24,64],"systems":[25],"is":[26],"increasingly":[27],"critical.":[28],"Log-probability-based":[29],"membership":[30,114],"inference":[31],"attacks":[32],"(MIAs)":[33],"have":[34],"become":[35],"a":[36,99],"widely":[37],"adopted":[38],"approach":[39],"for":[40],"assessing":[41],"data":[42],"exposure":[43],"large":[45],"language":[46],"models":[47],"(LLMs),":[48],"yet":[49],"their":[50],"effect":[51],"MLLMs":[53],"remains":[54],"unclear.":[55],"We":[56],"present":[57],"first":[59],"comprehensive":[60],"evaluation":[61],"extending":[63],"text-based":[65],"MIA":[66],"methods":[67],"to":[68],"multimodal":[69],"settings.":[70],"Our":[71],"experiments":[72],"under":[73],"vision-and-text":[74],"(V+T)":[75],"and":[76,83],"text-only":[77],"(T-only)":[78],"conditions":[79],"across":[80,96],"DeepSeek-VL":[82],"InternVL":[84],"model":[85],"families":[86],"show":[87],"that":[88],"in-distribution":[90],"settings,":[91,106],"logit-based":[92],"MIAs":[93],"perform":[94],"comparably":[95],"configurations,":[97],"with":[98],"slight":[100],"V+T":[101],"advantage.":[102],"Conversely,":[103],"out-of-distribution":[105],"visual":[107],"inputs":[108],"act":[109],"regularizers,":[111],"effectively":[112],"masking":[113],"signals.":[115]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-05T00:00:00"}
