{"id":"W4408133719","doi":"10.18280/mmep.120219","title":"Behavior of Visual Content in Deepfake Generated Based on Conditional Generative Adversarial Networks","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Generative grammar; Adversarial system; Content (measure theory); Computer science; Generative adversarial network; Artificial intelligence; Machine learning; Econometrics; Mathematics; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000513244,0.0003871905,0.0004203824,0.0004433317,0.0003244337,0.0006750665,0.000599918,0.0009226974,0.003273241],"category_scores_gemma":[0.004353302,0.0003204565,0.0003540539,0.0002185119,0.0005550883,0.001340061,0.000854622,0.001049521,0.0004443101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000901044,"about_ca_system_score_gemma":0.0004026681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003570256,"about_ca_topic_score_gemma":0.00291182,"domain_scores_codex":[0.9998227,0.00003560706,0.000004608434,0.00004870553,0.00005034255,0.00003809838],"domain_scores_gemma":[0.9988318,0.0007265317,0.00007084235,0.0001042089,0.0001856991,0.00008090729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002948397,0.00009087484,0.001537369,0.00006242747,0.00003513552,0.0002546042,0.000103895,0.9250889,0.03012221,0.01440136,0.001439106,0.02656921],"study_design_scores_gemma":[0.000002564395,0.00001266001,0.0002299999,0.000003190303,0.000001923925,0.00001834236,0.00000767394,0.9949613,0.002356517,0.002321878,0.00008063497,0.000003382762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6208777,0.0003162433,0.3694212,0.0007319701,0.0001482985,0.00006936676,0.0003675422,0.001165371,0.00690221],"genre_scores_gemma":[0.9829045,0.00004696843,0.01287253,0.00008020471,0.000007625499,0.00002062515,0.0002188932,0.0001149438,0.003733779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003570256,"threshold_uncertainty_score":0.01095009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02524425503877913,"score_gpt":0.2270222837456355,"score_spread":0.2017780287068563,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}