{"id":"https://openalex.org/W7163072037","doi":"https://doi.org/10.48550/arxiv.2605.31192","title":"The Regularizing Power of Language-Training Deepfake Detectors","display_name":"The Regularizing Power of Language-Training Deepfake Detectors","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163072037","doi":"https://doi.org/10.48550/arxiv.2605.31192"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31192","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.2605.31192","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120646383","display_name":"Benedikt Hopf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hopf, Benedikt","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137520488","display_name":"Zongwei Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zongwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137572290","display_name":"Radu Timofte","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Timofte, Radu","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.37950000166893005,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.37950000166893005,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2371000051498413,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11230000108480453,"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/overfitting","display_name":"Overfitting","score":0.9269000291824341},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5841000080108643},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.5652999877929688},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5475999712944031},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5223000049591064},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5128999948501587},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.48080000281333923},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.46219998598098755}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9269000291824341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7562999725341797},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6952000260353088},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6218000054359436},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5841000080108643},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.5652999877929688},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5475999712944031},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5223000049591064},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5128999948501587},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.48080000281333923},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.46219998598098755},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.33149999380111694},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3089999854564667},{"id":"https://openalex.org/C14103023","wikidata":"https://www.wikidata.org/wiki/Q11681459","display_name":"Pairing","level":3,"score":0.29829999804496765},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.27300000190734863},{"id":"https://openalex.org/C5465570","wikidata":"https://www.wikidata.org/wiki/Q5326898","display_name":"Early stopping","level":3,"score":0.2590999901294708}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31192","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.2605.31192","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31192","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":"Quality Education","score":0.7805770635604858,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"thanks":[1],"to":[2,13,39,53,91,133,136,156,176],"the":[3,37,89,124,154,174],"advent":[4],"of":[5,127],"Multimodal-LLMs,":[6],"deepfake":[7,47],"detectors":[8],"are":[9,199],"striving":[10],"not":[11],"only":[12,163],"be":[14,27,75],"generalizable":[15],"but":[16],"also":[17],"interpretable.":[18],"We":[19,97],"propose":[20],"that":[21,60,63,73,123,152],"these":[22],"two":[23],"challenges":[24],"can":[25,74,81,129],"effectively":[26,130],"tackled":[28],"jointly,":[29],"since":[30],"describable":[31],"artifacts":[32,72],"typically":[33],"generalize":[34],"better,":[35],"opening":[36],"possibility":[38],"use":[40,82,92],"language":[41,68],"as":[42],"a":[43,99,103,108,113,118,148,189,215],"regularization":[44],"mechanism.":[45],"Since":[46],"detection":[48],"generally":[49],"suffers":[50],"from":[51],"overfitting":[52,135],"low-level":[54,93],"domain-specific":[55],"artifacts,":[56],"our":[57,209],"intuition":[58],"is":[59],"an":[61],"LLM":[62],"has":[64],"been":[65],"pretrained":[66],"on":[67,205],"would":[69],"prefer":[70],"high-level":[71,83],"described":[76],"better.":[77],"This":[78],"way,":[79],"we":[80,146,171],"features":[84,94,132],"where":[85,95],"possible,":[86],"while":[87],"training":[88,115],"model":[90,155,175],"necessary.":[96],"utilize":[98],"dual-encoder":[100],"architecture,":[101],"pairing":[102],"frozen":[104],"specialist":[105],"detector":[106],"with":[107],"LoRA-tuned":[109],"MLLM":[110],"encoder,":[111],"and":[112,143,188],"two-stage":[114],"curriculum:":[116],"first,":[117],"binary":[119,164],"alignment":[120],"phase":[121],"demonstrates":[122],"intrinsic":[125],"capability":[126],"MLLMs":[128],"combine":[131],"mitigate":[134],"dataset-specific":[137],"artifacts.":[138],"To":[139],"further":[140,190],"bolster":[141],"generalization":[142],"achieve":[144],"interpretability,":[145],"employ":[147],"reinforcement":[149],"learning":[150],"stage":[151],"encourages":[153],"generate":[157],"descriptive":[158],"reasoning":[159,197],"before":[160],"classifying,":[161],"using":[162],"labels.":[165],"By":[166],"rewarding":[167],"this":[168,182],"\"explain-then-classify\"":[169],"behavior,":[170],"explicitly":[172],"incentivize":[173],"prioritize":[177],"high-level,":[178],"robust":[179],"features.":[180],"Crucially,":[181],"process":[183],"yields":[184],"both":[185],"interpretable":[186],"descriptions":[187],"boost":[191],"in":[192],"cross-dataset":[193],"performance,":[194],"even":[195],"when":[196],"chains":[198],"omitted":[200],"at":[201],"inference.":[202],"Extensive":[203],"experiments":[204],"benchmark":[206],"datasets":[207],"validate":[208],"approach,":[210],"outperforming":[211],"state-of-the-art":[212],"methods":[213],"by":[214],"large":[216],"margin.":[217]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}
