AI生成视频检测从视觉取证走向事实保真验证

1

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

DylanXinmingHou1,JuntianZhang2,XuGu2,

YichenWu3,NilsLukas1,GusXia1,XiuyingChen1,YuhanLiu1†

1MBZUAI,2GaolingSchoolofArtificialIntelligence,RenminUniversityofChina,

3HarvardUniversity

dyxhou@

,{nils.lukas,gus.xia,xiuying.chen,

yuhan.liu}@mbzuai.ac.ae

,{zhangjuntian,

guxu}@

,

yiwu6@

arXiv:2607.10787v1[cs.CV]12Jul2026

TheevolvingrealismofAI-generatedVideos(AIGC-V)israpidlyrenderingtradi-tionalartifact-centricdetectioninsufficient,necessitatingaparadigmshiftfromlow-levelinspectiontohigh-levelsemanticverification.ThispaperpresentsacomprehensivesurveyofAIGC-Vdetection,reframingthetaskasFactualFidelityVerification,whichaskswhethertheevents,entities,andphysicalprocessesde-pictedinavideoareconsistentwithreal-worldfacts.Tosystematizethisrapidlyevolvingfield,weproposeaVision-LanguageDual-Viewtaxonomythatorganizesexistingmethodsintoahierarchical,four-layerlandscape,spanningintrinsiccueanalysis,spatiotemporalconsistencymodeling,cross-modalconsistencyreasoning,andlanguage-guidedworld-levelreasoning.Thisdual-viewframinghighlightsafundamentaltransitionfromartifactmatchingintraditionaldeepfakedetectiontoevidence-basedsemanticverificationenabledbyvision-languagemodelsandagen-ticreasoningpipelines.Basedonasystematicreviewof221works,wesynthesizeAIGC-Vgenerationparadigms,surveythelandscapeofdetectionmethods,andreviewevaluationmetricsandbenchmarksinlinewithproposedviews.Finally,wediscusscurrentchallengesandidentifypromisingdirectionstowardrobust,explainable,andtrustworthydetection.

Date:July14,2026

ōGithub:

/dxhou/AI-Generated-Video-Detection

猾Homepage:

https://aigcvdetection.github.io/

1Introduction

Therapidevolutionofvideogenerationmodels,exemplifiedbySora2(

OpenAI

,

2024b

),Veo3(

GoogleDeepMind

,

2025

),andSeedance2.0(

ByteDanceSeed

,

2026

),isfundamentallyreshapingtheinformationlandscape.Unlikeearlydeepfakesdominatedbylocalizedfaceswapping(

Wangetal.

,

2024b

),modernAI-GeneratedContent-Videos(AIGC-V)haveachievedcinematicfidelitywithcoherentnarratives,increasinglyblurringtheboundarybetweensynthesizedfictionandcapturedreality.Thistechnologicalleapdestabilizesthefoundationaltrustinvideoevidence,traditionallyreliedupontoverify"who,when,where,andwhat"(

Hoetal.

,

2022a

).

†Correspondingauthor:YuhanLiu(

yuhan.liu@mbzuai.ac.ae

)

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

2

OUTPUT

FinalDecision:

AI-GeneratedVideo

RepresentativeMetrics:

FrameAUC=0.98TemporalF1=0.91

VideoAUC=0.95Text–VideoSim=0.34

Reasoning:

Worldknowledgerevealsnon-existentsponsornamesonrink-sidebillboards,andthecat’smotionontopoftheskater’sheadviolatesbasicphysicallawssuchasgravityandbalance.Togetherwiththelowtext–videoconsistencyscore,indicatinganAI-GeneratedVideo.

EvidenceAggregation:

•Frame-levelartifactsdetected

•Temporalinconsistencyacrossframes

•Text–videosemanticconsistencyviolations

•Violationsofreal-worldentitiesandphenomena

LanguageView

Sora

Prompt:

figureskaterperformsatripleaxle

withacatonherhead

GENERATION

Traditional

LargeModelEra

FaceSwap

DETECTION

VisualView

Figure1AnexampleoftheAIGC-Vdetectionpipelineunderourapproach,illustratingAI-generatedvideosfromtraditionalmethodsortext-to-videoprompts,detectionfromthevisualandlanguageviews,andoutputsatdifferentlevels.

Asgenerationparadigmsshiftfromlocalmanipulationtoend-to-endsynthesis,suchastext-to-video,traditionaldetectionmethods(

Wangetal.

,

2025d

;

Maetal.

,

2025

)faceacriticalbottleneck.Earlydetectionsystemsprimarilyreliedonlow-levelvisualartifacts,suchasblendingboundaries.However,advanceddiffusionmodelsandtransformers(

Ho

etal.

,

2022b

;

OpenAI

,

2024a

)cannowproducevisuallyhigh-fidelityvideos.Thisparadigmshiftnecessitatesanewdetectionlandscape,shiftingfromperceptualinspection(checkingforvisualartifacts)tocognitivereasoning(checkingforsemanticandfactualviolations).ThesurgeofVision-LanguageModels(VLMs)(

Zhangetal.

,

2025d

,

c

;

Wangetal.

,

2026

)andagenticframeworks(

Liuetal.

,

2024b

,

2025g

,

f

;

Houetal.

,

2024

)offersapromisingpathway,enablingdetectorstoperformworld-levelreasoningandverifycross-modalconsistency.Although

Fuetal.

(

2025

)and

Parketal.

(

2025

)havebeguntoexplorethesedirections,theexistingliteratureremainsfragmented.Meanwhile,surveyslike

Peietal.

(

2024

)and

Liuetal.

(

2025c

)areconstrainedtotheearlyeraofdeepfakesortreatvideomerelyasasequenceofimages,failingtosystematizetheemergingclassofmethodsthatleveragelanguageforsemanticverification.

Toaddressthisgap,weproposeaVision-LanguageDual-ViewtaxonomytoorganizetheAIGC-Vdetectionlandscape,asillustratedinFigure

1

.WereframetheproblemasFactualFidelityVerification,determiningwhetherthevideocontentalignswithreal-worldfactsandphysicallaws,ratherthansimplebinaryclassification.Ourframeworkcategorizesdetectionmethodsintoafour-layerhierarchy,progressingfromlow-levelperceptiontohigh-levelcognition:(1)IntrinsicCueAnalysis,(2)SpatiotemporalConsistency,(3)Cross-ModalConsistency,and(4)Language-GuidedWorld-LevelReasoning.

Inthissurvey,wecomprehensivelyreview221worksasofMarch2026.WebeginwithAIGC-Vparadigms(§

2

),thenformulateAIGC-VdetectionfromtheperspectiveoffactualfidelityandintroduceourVision-Languagedual-viewframing(§

3

).Wesystematicallyorganizemethodsunderthislandscape(§

4

),revisitevaluationmetrics(§

5.1

)fromadual-view,andreviewbenchmarksinlinewithAIGC-Vparadigms(§

5.2

).Finally,weidentifythecriticalchallenges(§

6

)indetectingincreasinglysophisticatedAIGC-V.Bybridgingthegapbetweentraditionalvisualforensicsandemergingmultimodalreasoning,thispaperaimstoprovideastructuredroadmapforthenextgenerationofexplainableandtrustworthyAIGC-Vdetection.

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

3

GenerativeVideoSynthesis

•Text-to-VideoSynthesis

•Image-to-VideoSynthesis

•Long-formVideo

Audio-VisualEditing

•Audio-conditionedTalkingHeads

•LipSynchronization

•VoiceReplacement

LocalManipulation

•FaceSwapping

•AttributeEditing

•BackgroundAltering

Figure2OverviewofthreeAIGC-Vparadigmsdefinedinthissurvey,highlightingtypicalforms.

2ParadigmsofAI-GeneratedVideo

Currently,diffusion-basedandTransformer-basedarchitecturesareadvancingrapidlyinvideogeneration,drivingmajorprogressintext-to-videoandotherAIGC-Vpipelines(

Ho

etal.

,

2022b

,

a

;

Singeretal.

,

2022

;

OpenAI

,

2024b

;

GoogleDeepMind

,

2025

;

ByteDanceSeed

,

2026

).Inthissurvey,wegroupmainstreammethodologiesintothreecategoriesaccordingtotheunderlyinggenerationparadigm:localmanipulation,audio-visualediting,andgenerativevideosynthesis,followingrepresentativesystemsineachregime(

Brisonetal.

,

2025

;

Chengetal.

,

2022b

;

Hoetal.

,

2022b

).Figure

2

providesanoverview.

2.1LocalManipulation

MethodologiescategorizedasLocalManipulationtypicallyoperateonauthenticvideosequencesbymodifyingspecificspatialregionsordistinctsemanticattributes(

Brison

etal.

,

2025

;

HeoandWoo

,

2025

).Theresultinggenerationmaintainsahighdegreeofstructuralfidelitytotheoriginalfootage.Anothertechnicalparadigmcentersonvideofaceswappingandfacialelementmanipulation.Representativeapproachestypicallyutilizediffusionmodels(

Wangetal.

,

2024b

),3Dfacialpriors(

Wangetal.

,

2025d

),andcontrollableconditionalencoding,integratingidentityfeaturetransferwithexpression,pose,andilluminationreconstructionfromsourcevideosintoaunifiedgenerativeframework.Giventheireaseofimplementationandcovertnature,thesetechniquesrepresentacriticalthreatinreal-worldadversarialscenarios.

2.2Audio-VisualEditing

Audio-VisualEditingusesspeechasanexplicitcontrolsignaltodrivetalking-headgener-ationortore-dubexistingvideos,requiringtightcross-modalalignmentbetweenaudioandfacedynamicswhilepreservingidentityandbackground(

Chengetal.

,

2022b

).Andrecentdiffusion-basedor3D-awareapproaches(

Maetal.

,

2025

;

Hongetal.

,

2025

)movetowardunifiedconditionalgeneration,whereaudioguidanceandidentityconstraintsarejointlymodeledtoproducelip-synced(

Lietal.

,

2024a

),visuallyconsistentoutputs.Duetotheirlowoperationalbarrierandstrongperceptualplausibility,audio-visualeditscanconvincingly“bind”forgedvisualperformancestoplausiblevoicetracks,amplifyingrisksinimpersonationandmisinformation.

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

4

2.3GenerativeVideoSynthesis

GenerativeVideoSynthesistargetsend-to-endcreationofcompletevideosequencesfromtextornoise,fabricatingbothappearanceanddynamicswithoutauthenticcarriers.Diffusion-basedgeneratorsextendimagediffusionwithtemporalmodelingforcoherence,exem-plifiedbyVideoDiffusionModels(

Hoetal.

,

2022b

)andcascadedhigh-fidelitypipelinessuchasImagenVideo(

Hoetal.

,

2022a

);Make-A-Video(

Singeretal.

,

2022

)furtherre-ducesrelianceonpairedtext-videodatabyleveragingtext-imagepriorsandunpairedvideos.Recentarchitecturesfurtheroptimizerepresentations:Show-1(

Zhangetal.

,

2025b

)hybridizespixel-basedgenerationwithlatentrefinementforefficiency,whileGridDiffu-sion(

Leeetal.

,

2024

)unifiesspatiotemporaldimensionsintoasingle2Dgridtosimplifymodeling.Transformer-stylesynthesistokenizesvideosandlearnslong-horizonstruc-tureviaautoregressiveormaskedmodeling,whileDiTfurtherhighlightsthescalabilityofTransformerbackbonesfordiffusion-basedgeneration(

PeeblesandXie

,

2023

).Attheindustrialfrontier,large-scaletext-to-videosystemssuchasSora2(

OpenAI

,

2024b

),Seedance2.0(

ByteDanceSeed

,

2026

),andVeo3(

GoogleDeepMind

,

2025

)demonstratestrongopen-domaincapabilityandraiseurgentneedsforprovenanceandverification.

WheretheEvidenceLives

>LMVkeepsarealcarrier,sothestrongestcluesareusuallyresidualandspatiallylocalizedratherthanscene-wide.

>AVEisconstrainedbycoupling,makingsynchrony,speakerconsistency,andspeech-conditionedfacialmotionmoredecisivethangenericimageartifacts.

>GVSremovesthecarrieraltogether,shiftingdetectionawayfromlocaledittracestowardlong-rangecoherence,factualverification,andprovenance.

3AI-GeneratedVideoDetection

3.1TaskScope

VisuallyhighfidelityAIGC-Vwithrichernarrativesmakethereliabilityofmethodsthatoutputonlya“real/fake”probabilityincreasinglylowinsecurityandprivacyscenar-ios(

Wangetal.

,

2024a

;

Kauretal.

,

2024

),andthedetectiontaskshouldshiftfrommerelyansweringthetraditionalquestionof“whetherthevideoisAIgenerated”tofact-leveljudgmentofwhetherthevideocontent“objectivelyhappenedintherealworld.”

Fromafact-levelperspective,videocontentcanbeabstractedasaseriesofpropositionsoftheform“atacertaintimeandplace,whichentitiesexist,andwhathappenstotheseentities.”WedefineFactualFidelityas“thefact-levelpropositionsimpliedbythevideoanditsmetadataareconsistentwiththerealworld,”andtreatFactualFidelityVerificationasthefundamentalobjectiveofAIGC-Vdetection.

Furthermore,differentevidencepathwaysprovidedbydetectionmethodsareintegratedtoindicateatwhichlevelsthevideoviolatesfactualfidelity(

Zhangetal.

,

2024b

;

Yuetal.

,

2025

;

Shenetal.

,

2025

)andformaninterpretableandtrustworthyevidencespace.

Definition3.1(Factualfidelityverification).Letx∈Xbeavideosampletogetherwithits

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

5

VisualView

ProbabilityDensity

IntrinsiccuesAnalysis(section4.1)

1.Pixel&GeometricArtifacts2.PhysiologicalFeatures

Layer1

RealHeart(Varied)

Robo-Heart(TooPerfect!)

3.RepresentationDistribution

Discrepancy

RealDiscrepancy/ShiftForged

RepresentationRepresentation

3DCNN/Transformer

LanguageView

Layer2SpatiotemporalConsistency(Section4.2)

1.TemporalandMotionInconsistencies

2.PhysicalandFrequencyArtifacts

InputVideoFFT{RFaekael/

ResidualMaps

MotionFeatures

TemporaEncoder

InputFrames

InconsistencyMap

Fusion

3.HumanBehavioralandInteraction

Dynamics

--l/

ActionUnits

Layer3cross-Modalconsistency(section4.3)

1.Audio-visualconsistencyDetection

2.Text-visualsemanticsconsistencyReasoning

Video

Aal

Text

giInstanciictlLIbCaosnedsisVteerndcyic-t

SemanticConsistencyReasoning

(Metadata&Transcripts)

3.RobustLearningandTemporal

Localization

Audio

VideoFrames

/

Pre-trainingwithRealVideos

RealVideos

Pre-trainedModel

Layer4Language-GuidedWorld-LevelReasoning(Section4.4)

1.Language-CalibratedForgery

Representation

2.Tool-AugmentedAgenticPipelines

3.Post-TrainingforExplainableWorld-LevelReasoning

DatasetVerdictLabels

BEancckobdoeners

ArtifactAnalyzer

Cross-modal

Tools

Forgery-aware

RepresentationSpace

Reasoning

Evidences

Post-trainingPeriodInferencePeriod

Reasoning&Act

Detector

Explanation

Video

Trained

Model

Video

Facial

CalibrationModules

World-LevelKnowledge

Video

StructuredReasoning

ReprogrammingVLM

ConstraintsNewsFacts

AgenticLoop

SFTPost-training

RLPost-training

DatasetCollection

LanguagePriors

BaseModel

Landmarsks

Call&Build

Evidences

Observe

Tools

VLMs

RAG

Figure3Dual-view,four-layerframeworkforAIGC-Vdetection.Thevisualview,spanningLayers1-2,modelsintrinsiccuesandspatiotemporalconsistency,whereasthelanguageview,spanningLayers3-4,addressescross-modalconsistencyandworld-levelreasoning,witheachlayerfurtherdecomposedintofine-grainedsubcategories.Boundarynote:Layer1usesframe-leveldistributionalevidence;Layer2modelsframe-sequencerelations;Layer3performswithin-videomultimodalverification;Layer4requiresexternalknowledgeforverification.

metadata,andletΠ(x)⊆Pdenotethesetoffact-levelpropositionsimpliedbyx.Definetheidealfactual-violationlabelasy(x)=1[Π(x)⊈Pworld],i.e.,y(x)=1ifandonlyifxviolatesfactualfidelity.

Definition3.2(AIGC-Vdetection).AnAIGC-VdetectorisamappingD:X→[

0

,

1

]×E.Foravideosamplex∈X,D(x)containsasuspicionscores(x)∈[

0

,

1

]andevidencee(x)∈Esupportingitsjudgment.

Werequiree(x)tobetrustworthyandgrounded.Concretely,werecommendrepresenting

e(x)asafinitesetofevidenceitemse(x)={εk}=1,whereeachεk=(gk,ck,πk,rk)

contains:(a)agroundinggk,suchastemporalsegmentandoptionalspatialtrack,(b)aconfidenceπk∈[

0

,

1

],(c)apossiblehuman-readableclaim-checkstatementckdescribingwhatsub-claimisbeingverifiedandwhatinconsistencyisfound,and(d)aprovenancerecordrksuchastooloutputsorretrievedfactsenablingtraceability.

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

6

3.2Dual-viewFour-LayerTaxonomy

ThemethodologicalperspectivesofAIGC-Vdetectioncanbesummarizedastwocomple-mentaryscientificviews:visualandlanguage.Outofthisobservation,asillustratedinFigure

3

,weproposeaVision-LanguageDual-Viewapproachandorganizeexistingmeth-odsintoafour-layermethodologicallandscapefromlow-levelperceptiontohigh-levelcognitionin§

4

.

Definition3.3(Multimodalvideosample).LetI⊆RH×W×Cdenotethespaceofframes,andletA,S,Mdenotethespacesofaudio,text,andmetadata.Amultimodalvideosampleis

x=(V,A,S,M)∈X:=IT×A×S×M,

whereV1isthevisualstream,andA,S,Mmaybeemptywhenthecorrespondingmodalityisunavailable.

VisualView.ThevisualviewfocusesonthevisualmodalityandemphasizesstatisticaldifferencesbetweenAIGC-Vandrealvideos(

Zhouetal.

,

2024

;

Guetal.

,

2021

).Thedetectionpathwayextendsfromlow-levelintrinsiccueanalysisattheframelevelin§

4.1

tospatiotemporalconsistencyacrossframesin§

4.2

,formingtheperceptualperspectiveforfactual-fidelityverification.

LanguageView.Inthissurvey,thelanguageviewdenotesagroundedverificationpathway,notanassumptionthatallaudiocuesarelinguisticbydefault.Concretely,audioevidenceentersthispathwayintwoforms.Thefirstformisnon-linguisticcross-modalperceptualevidence,whichmainlycheckswithin-videoalignmentwithoutsemanticparsing,suchaslip–speechsynchronyandonsetandoffsetconsistency(

Chughetal.

,

2020

;

Yangetal.

,

2021

).Thesecondformislanguage-groundedspeechevidence:speechcanbemappedviaASRorspeechrepresentationstophoneme,word,orsemanticunits,enablingconsistencychecksagainstidentityandtextcontent,suchasvoiceprint–identitycoherenceandspeech–subtitleagreement(

Shahzadetal.

,

2022

;

BohacekandFarid

,

2024

;

Chengetal.

,

2022a

;

Zhangetal.

,

2025f

).Thisdesignchoiceclarifieswhyaudiocanlegitimatelyparticipateinthelanguageview:itprovidesaninterfacefromacousticsignalstolanguage-levelevidenceandtextualreasoningwhenneeded.Buildingonthesetwocuetypes,methodsprogressfromcross-modalconsistencyanalysis(§

4.3

)toworld-levelreasoningatfactualandknowledgelevelswhenexternalevidenceisrequired(§

4.4

)(

Zhangetal.

,

2024b

;

Yuetal.

,

2025

;

Shenetal.

,

2025

),therebyformingthecognitiveperspectiveoffactual-fidelityverification.

WhatAIGC-VDetectionMustEstablish

>Thetargetisfactualfidelity,notonlyareal/fakescore:detectionshouldjudgewhetherthefact-levelclaimsimpliedbyavideoanditsmetadataremainconsistentwiththerealworld.

>Adetectorshouldreturnevidence,notonlysuspicion:theoutputshouldcoupleascorewithgrounded,traceableevidenceshowingwhichclaimisbeingchecked,wheretheinconsistencylies,andwhythedecisionisreliable.

>Evidencecanarrivethroughtwocomplementarypathways:thevisualviewtestsintrinsicandtemporalplausibility,whilethelanguageviewmovesfromwithin-videocross-modalverificationtoexplicitclaimcheckingandworld-levelreasoning.

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

7

Time

DeepFakeDisrupterFreqBlender

Region-AwareTemporalFilter

NaturalConsistencyRepresentation

LatentDiffusion

Vision-LanguageAlignment

EfficientNet&ViTCombining

NeuralTalkingHeads

VisualTransformers

AVT2-DWF

NSG-VD

Sora-likeGenerator

ReasoningVLMs&Agents

Cross-ModalAlignmentandDistillation

DeeptraceReward

VidGuard-R1

EDVD-LLaMA

DeepRhythm

DeepFakesON-Phys

Spatial-TemporalInconsistency

Learning

AppearanceandBehavior

GAN-basedGeneration

DeepCNNs&RNNs

Aural-Oral

Layer1

Layer2

Layer3

Layer4

UpperLine

GenerativeModels

RacingFlags

UnderLine

FoundationModels

Voice-FaceHomogeneity

Audio-VisualAttention

ChatGPTDetectDeepFakes

DeMamba

202020212022202320242025

Figure4LandscapeofrepresentativeAIGC-Vdetectionmethodsalignedwiththefour-layertaxonomyin§

4

.Methodsaregroupedbytheevolvingcapabilitiesofgenerativemodelsthatposeemergingthreatsandthecorrespondingadvancesinfoundationmodelsthatenabledetection.Theyear-wiseshifttowardlanguage-viewmethodsacrossthesurveyeddetectionpapersisquantifiedinTable

5

.

4LandscapeofDetectionMethods

AsillustratedinFigure

4

,weorganizeAIGC-Vdetectionmethodsintofourlayers,andprovideastructuredoverviewinthissection.Ourtaxonomyisevidence-orientedratherthantask-exclusive:layerassignmentfollowsthedominantevidencepathwayusedfordecision-making,notahardpartitionoftasks.Tomakelayerassignmentoperational,weuseboundarycriteriatiedtoevidencedependency:Layer1capturesframe-leveldistributionalcuesthatcanbescoredperframeandpooled,whereasLayer2requiresexplicitframe-sequencemodelingofspatiotemporalrelations.Layer3thenmovesfromvisual-onlycoherencetowithin-videomultimodalverification,whereasLayer4furtherverifiesclaimsagainstexternalfacts,commonsense,orphysics,evenwhenwithin-videomodalitiesappearmutuallyconsistent.Whenamethodcombinescuesfrommultiplelayers,wemapeachmoduletoitsprimaryevidencetyperatherthanforcingasingle-tasklabel.Foreachlayerbelow,wefurthersummarizetheassumption-signal-failure-modelinkagetoclarifywhyeachpathwayworks,whenitbreaks,andhowrecentmethodsimproverobustnessunderthefactual-fidelityframinginSection

3

.Tomakethistrendconcrete,Table

5

summarizesthesurveyeddetectionpapersinSection

4

usingcompletepublicationyearsthrough2025.

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

8

4.1Layer1:IntrinsicCuesAnalysis

Layer1askswhetherlow-levelvisualsignalsobeythestatisticalregularitiesofrealvideos,whethertheymatchacquisitionandpost-processingeffects(

LeandWoo

,

2023

;

Wangetal.

,

2022b

),andwhetherAIgenerationoreditingleavesintrinsiccuessuchasstylepatterns,modelartifacts(

Chenetal.

,

2021

),ordeviantphysiologicalsignals(

Ciftcietal.

,

2020b

;

Qietal.

,

2020

;

Ciftcietal.

,

2020a

).Methodsoperatefromthevisualviewbymodeling,extracting,andamplifyingtheselow-levelsignals(

Vahdatietal.

,

2024

).Thedominantevidenceunitremainsframe-levelanddistributional:asingleframecanalreadyyieldanindependentsignal,whilemultipleframesmainlyaggregateper-frameevidence.Oncethedecisiondependsonexplicitinter-framerelations,theevidencemovestoLayer2.Table

1

summarizesrepresentativemethodsinthislayer.

PixelandGeometricArtifacts.ThislineofworktargetsintrinsictracesintroducedbyAIGC-Vsynthesisandpost-processing,includingfrequencyfingerprints,textureresidues,andgeometricinconsistencies.FreqBlenderamplifiessynthesis-relatedspectralcuesviaspectralblendingandcontrastivelearning(

Zhouetal.

,

2024

).Suchframe-wisefrequencyfingerprintsremainLayer1evidence,whereasdescriptorsthatdependontemporal-frequencybehaviorbelongtoLayer2.Localizeddesignsreducerelianceonglobalspectrabyfocusingonregion-levelresidues,suchasmask-guidedlocalizationinMagDRandtexture-consistencylearninginHCIL(

Chenetal.

,

2021

;

Guetal.

,

2022b

).Geometriccuescomplementspectralandtexturecuesbytestingstructuralplausibility,asinstantiatedbyLRNet,RAM,andhead-poseinconsistencytests(

Sunetal.

,

2021

;

Tianetal.

,

2024

;

Yang

etal.

,

2019

).Followingquality-awaretrainingresults(

LeandWoo

,

2023

),thesecuesstilldegradeundercompressionandadaptivefilteringinlow-qualitypipelines.

PhysiologicalFeatures.Physiologicalcuestreatfacesascarriersofbiologicalrhythmsandmicro-dynamicsthataredifficulttosynthesizeconsistently.BecausemanypracticalAIGC-Vscenariosremainhuman-centric,especiallytalkingheadsandavatars,wekeepphysiologicalevidenceasadedicatedsubclass.Recentfollow-upisstilllimited:beyondclassicrPPGpipelines,onlyafewworksrevisitlocal-globalphysiologicalinteractionpatterns(

Wuetal.

,

2024

;

D’Amelioetal.

,

2023

).Heart-relatedsignalsareextractedeitherbymappingresidualrepresentationstoheart-ratedomains(

Ciftcietal.

,

2020b

)orbylearningspatiotemporalattentionoverrPPG-relatedpatterns(

Qietal.

,

2020

).Higher-orderheart-ratevariabilityandcross-regioncouplingimproverobustnessundernoise(

Hernandez-

Ortegaetal.

,

2020

;

Fernandesetal.

,

2019

;

Leeetal.

,

2021

;

Wuetal.

,

2024

;

Stefanovetal.

,

2022

;

MaoandYang

,

2021

;

Ciftcietal.

,

2020a

).Complementaryevidenceincludesblinkirregularitiesandidentity-specificmuscledynamics(

Lietal.

,

2018

;

Cozzolinoetal.

,

2021

).Thesecuesrequirestablefacialvisibilityandsufficienttemporalsupport,andremainsensitivetoocclusionandaggressivere-encoding.

RepresentationDistributionDiscrepancy.Thislineofworktargetscross-generatortrans-ferbymodelingdistributiongapsbetweenauthenticandsyntheticvideosratherthancommittingtoafixedartifactfamily.Latentandstyle-spaceperturbationsdiversifysyn-theticstylesandreduceforgeryspecificity(

Yanetal.

,

2024

),whilelatent-flowmodeling

DetectingAI-GeneratedVideo:AVision-LanguageDual-ViewSurvey

9

capturesabnormalstyletrajectories(

Choietal.

,

2024

).CurriculumlearningandsyntheticBlend-Fakedataexposemodelstocontrolleddifficultyandreduceshortcutlearning(

Lin

etal.

,

2024b

;

Chengetal.

,

2024

).Recentfollow-upstaysclosetolow-levelevidence:DFAcombinesfoundation-modelglobalpriorswithexplicitlocalanomalyanalysistoimprovegeneralizationunderevolvingforgerypatterns(

Liaoetal.

,

2026

).Discrepancyobjectivesseparateoverlappingrealandfakemanifoldswithboundedcontrastivelearningandexplicitlymodeledappearanceshifts(

Larueetal.

,

2023

;