{"id":"W4224052715","doi":"10.1109/accai53970.2022.9752506","title":"Deep Fakes Image Animation Using Generative Adversarial Networks","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Adversarial system; Face (sociological concept); Animation; Identifier; Idiot; Closeness; Class (philosophy); Computer vision; Computer graphics (images); Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007088932,0.000241436,0.0002615633,0.0002646623,0.0007240388,0.0004236945,0.001502029,0.00005138197,0.0001943332],"category_scores_gemma":[0.00004649815,0.0002590329,0.00005286571,0.0003692571,0.0001190419,0.001233763,0.001447997,0.0005581956,0.000006546178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002266044,"about_ca_system_score_gemma":0.00006820423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001858641,"about_ca_topic_score_gemma":0.00002388328,"domain_scores_codex":[0.9981408,0.0002223336,0.000705587,0.0002805532,0.0003847707,0.0002659489],"domain_scores_gemma":[0.9984035,0.0002227853,0.0005995044,0.0005347814,0.0001729017,0.0000664539],"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":[0.00003365316,0.00006317571,0.00003532248,0.000005363262,0.00001692556,5.904584e-7,0.001923666,0.5906419,0.0001711382,0.3695249,0.00009832982,0.0374851],"study_design_scores_gemma":[0.0006971372,0.00005691526,0.00008122089,0.0000259349,0.000004963907,0.000007653058,0.001905201,0.9847633,0.0001596296,0.008323628,0.003703752,0.0002707202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003005635,0.0002880172,0.96047,0.0004866797,0.0005740967,0.0003382133,0.000008398557,0.00007222297,0.03475671],"genre_scores_gemma":[0.8223271,0.0006087907,0.176093,0.0007081248,0.00007857053,0.00004502653,0.00009566096,0.00001078876,0.00003301345],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8193215,"threshold_uncertainty_score":0.9999862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139146744812673,"score_gpt":0.285105862677837,"score_spread":0.2637143952297102,"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."}}