{"id":"https://openalex.org/W7160317358","doi":"https://doi.org/10.1109/wacv61042.2026.00059","title":"M-ErasureBench: A Comprehensive Multimodal Evaluation Benchmark for Concept Erasure in Diffusion Models","display_name":"M-ErasureBench: A Comprehensive Multimodal Evaluation Benchmark for Concept Erasure in Diffusion Models","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160317358","doi":"https://doi.org/10.1109/wacv61042.2026.00059"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112491380","display_name":"Jian Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ju-Hsuan Weng","raw_affiliation_strings":["National Taiwan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113154053","display_name":"Jia-Wei Liao","orcid":"https://orcid.org/0000-0001-7328-0961"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jia-Wei Liao","raw_affiliation_strings":["National Taiwan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135283804","display_name":"Cheng-Fu Chou","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Cheng-Fu Chou","raw_affiliation_strings":["National Taiwan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Taiwan University","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010007692","display_name":"Jun-Cheng Chen","orcid":"https://orcid.org/0000-0002-0209-8932"},"institutions":[{"id":"https://openalex.org/I4210086894","display_name":"Research Center for Information Technology Innovation, Academia Sinica","ror":"https://ror.org/000zgvm20","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210086894","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jun-Cheng Chen","raw_affiliation_strings":["Academia Sinica,Research Center for Information Technology Innovation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Academia Sinica,Research Center for Information Technology Innovation","institution_ids":["https://openalex.org/I4210086894"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46031493,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"527","last_page":"536"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.11299999803304672,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.11299999803304672,"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/T12031","display_name":"Speech and dialogue systems","score":0.11079999804496765,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.07419999688863754,"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/benchmark","display_name":"Benchmark (surveying)","score":0.5235000252723694},{"id":"https://openalex.org/keywords/erasure","display_name":"Erasure","score":0.39399999380111694},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.3474000096321106},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.3156999945640564},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.26809999346733093},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.26460000872612}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.666700005531311},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5235000252723694},{"id":"https://openalex.org/C2778790127","wikidata":"https://www.wikidata.org/wiki/Q484885","display_name":"Erasure","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3912000060081482},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37450000643730164},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3156999945640564},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31279999017715454},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26739999651908875},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00059","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321041","display_name":"Academia Sinica","ror":"https://ror.org/05bxb3784"},{"id":"https://openalex.org/F4320331164","display_name":"National Science and Technology Council","ror":"https://ror.org/00wnb9798"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W4312933868","https://openalex.org/W4386071613","https://openalex.org/W4386072096","https://openalex.org/W4386076215","https://openalex.org/W4386076532","https://openalex.org/W4390871724","https://openalex.org/W4390873054","https://openalex.org/W4390874260","https://openalex.org/W4394593019","https://openalex.org/W4402667886","https://openalex.org/W4402713133","https://openalex.org/W4402715906","https://openalex.org/W4402727884","https://openalex.org/W4402916987","https://openalex.org/W4402959574","https://openalex.org/W4404612908","https://openalex.org/W4408155387","https://openalex.org/W4413145896","https://openalex.org/W4413146433","https://openalex.org/W4413146487","https://openalex.org/W4413155850","https://openalex.org/W7133185348","https://openalex.org/W7160197403","https://openalex.org/W7160200465"],"related_works":[],"abstract_inverted_index":{"Text-to-image":[0],"diffusion":[1],"models":[2],"may":[3],"generate":[4],"harmful":[5],"or":[6],"copyrighted":[7],"content,":[8],"motivating":[9],"research":[10],"on":[11,19],"concept":[12,70,199],"erasure.":[13],"However,":[14],"existing":[15,103],"approaches":[16],"primarily":[17],"focus":[18],"erasing":[20],"concepts":[21,51,151],"from":[22],"text":[23,77,110,202],"prompts,":[24,78],"overlooking":[25],"other":[26],"input":[27,75],"modalities":[28,44],"that":[29,67,102,148,163],"are":[30],"increasingly":[31],"critical":[32],"in":[33,128],"real-world":[34],"applications":[35],"such":[36],"as":[37],"image":[38],"editing":[39],"and":[40,81,92,118,154],"personalized":[41],"generation.":[42],"These":[43],"can":[45],"become":[46],"attack":[47],"surfaces,":[48],"where":[49],"erased":[50],"re-emerge":[52],"despite":[53],"defenses.":[54],"To":[55,132,186],"bridge":[56],"this":[57],"gap,":[58],"we":[59,88,136],"introduce":[60],"M-ErasureBench,":[61],"a":[62,145],"novel":[63],"multimodal":[64],"evaluation":[65,97],"framework":[66],"systematically":[68],"benchmarks":[69],"erasure":[71,107,200],"methods":[72,104],"across":[73],"three":[74],"modalities:":[76],"learned":[79,116],"embeddings,":[80],"inverted":[82,119],"latents.":[83],"For":[84],"the":[85,129,156,175,187,194],"latter":[86],"two,":[87],"evaluate":[89],"both":[90],"white-box":[91,130,178],"black-box":[93],"access,":[94],"yielding":[95],"five":[96],"scenarios.":[98],"Our":[99],"analysis":[100],"shows":[101],"achieve":[105],"strong":[106],"performance":[108],"against":[109],"prompts":[111],"but":[112],"largely":[113],"fail":[114],"under":[115,174],"embeddings":[117],"latents,":[120],"with":[121,205],"Concept":[122,143],"Reproduction":[123],"Rate":[124],"(CRR)":[125],"exceeding":[126],"90%":[127],"setting.":[131],"address":[133],"these":[134],"vulnerabilities,":[135],"propose":[137],"IRECE":[138,164],"(Inference-time":[139],"Robustness":[140],"Enhancement":[141],"for":[142,212],"Erasure),":[144],"plug-and-play":[146],"module":[147],"localizes":[149],"target":[150],"via":[152],"cross-attention":[153],"perturbs":[155],"associated":[157],"latents":[158],"during":[159],"denoising.":[160],"Experiments":[161],"demonstrate":[162],"consistently":[165],"restores":[166],"robustness,":[167],"reducing":[168],"CRR":[169],"by":[170],"up":[171],"to":[172],"40%":[173],"most":[176],"challenging":[177],"latent":[179],"inversion":[180],"scenario,":[181],"while":[182],"preserving":[183],"visual":[184],"quality.":[185],"best":[188],"of":[189,198],"our":[190,207],"knowledge,":[191],"M-ErasureBench":[192],"provides":[193],"first":[195],"comprehensive":[196],"benchmark":[197,208],"beyond":[201],"prompts.":[203],"Together":[204],"IRECE,":[206],"offers":[209],"practical":[210],"safeguards":[211],"building":[213],"more":[214],"reliable":[215],"protective":[216],"generative":[217],"models.":[218]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-06T00:00:00"}