{"id":"https://openalex.org/W7147644200","doi":"https://doi.org/10.48550/arxiv.2603.26984","title":"A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models","display_name":"A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7147644200","doi":"https://doi.org/10.48550/arxiv.2603.26984"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26984","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26984","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.2603.26984","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113284741","display_name":"Mujtaba Hussain Mirza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mirza, Mujtaba Hussain","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5131415281","display_name":"Antonio D'Orazio","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"D'Orazio, Antonio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083057067","display_name":"Odelia Melamed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Melamed, Odelia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132571147","display_name":"Iacopo Masi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masi, Iacopo","raw_affiliation_strings":[],"raw_orcid":null,"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":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":0.9251999855041504,"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":0.9251999855041504,"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":0.01549999974668026,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.010999999940395355,"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":0.8255000114440918},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7578999996185303},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7192000150680542},{"id":"https://openalex.org/keywords/closed-captioning","display_name":"Closed captioning","score":0.489300012588501},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.3452000021934509},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.30869999527931213}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8255000114440918},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7578999996185303},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7192000150680542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6517999768257141},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5685999989509583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4916999936103821},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.489300012588501},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.30869999527931213},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26984","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26984","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.2603.26984","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26984","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":[{"display_name":"Affordable and clean energy","score":0.6633560061454773,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Despite":[0],"the":[1,33,75,79,82,123],"rapid":[2],"progress":[3],"in":[4,25,89,97,127],"multimodal":[5],"models":[6,38],"and":[7,119,133],"Large":[8],"Visual-Language":[9],"Models":[10],"(LVLM),":[11],"they":[12],"remain":[13],"highly":[14],"susceptible":[15],"to":[16,42,54],"adversarial":[17,29,43],"perturbations,":[18],"raising":[19],"serious":[20],"concerns":[21],"about":[22],"their":[23],"reliability":[24],"real-world":[26],"use.":[27],"While":[28],"training":[30],"has":[31],"become":[32],"leading":[34],"paradigm":[35],"for":[36,113,121],"building":[37],"that":[39,73,92,107],"are":[40],"robust":[41],"attacks,":[44],"Test-Time":[45,66],"Transformations":[46],"(TTT)":[47],"have":[48],"emerged":[49],"as":[50,130],"a":[51,69,90,110],"promising":[52],"strategy":[53],"boost":[55],"robustness":[56,76,124],"at":[57,140],"inference.":[58],"In":[59],"light":[60],"of":[61,81,125],"this,":[62],"we":[63],"propose":[64],"Energy-Guided":[65],"Transformation":[67],"(ET3),":[68],"lightweight,":[70],"training-free":[71],"defense":[72,112],"enhances":[74],"by":[77],"minimizing":[78],"energy":[80],"input":[83],"samples.":[84],"Our":[85],"method":[86],"is":[87,138],"grounded":[88],"theory":[91],"proves":[93],"our":[94],"transformation":[95],"succeeds":[96],"classification":[98,116],"under":[99],"reasonable":[100],"assumptions.":[101],"We":[102],"present":[103],"extensive":[104],"experiments":[105],"demonstrating":[106],"ET3":[108],"provides":[109],"strong":[111],"classifiers,":[114],"zero-shot":[115],"with":[117],"CLIP,":[118],"also":[120],"boosting":[122],"LVLMs":[126],"tasks":[128],"such":[129],"Image":[131],"Captioning":[132],"Visual":[134],"Question":[135],"Answering.":[136],"Code":[137],"available":[139],"github.com/OmnAI-Lab/Energy-Guided-Test-Time-Defense":[141],".":[142]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-02T00:00:00"}