{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007345382,0.0007968568,0.0005198031,0.0004012925,0.0002966382,0.0008535555,0.0008269815,0.001021646,0.004831031],"category_scores_gemma":[0.002181113,0.0003849046,0.0008043921,0.000215202,0.0008497002,0.001036393,0.001197484,0.001993196,0.0007770165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009750879,"about_ca_system_score_gemma":0.0004707196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003461994,"about_ca_topic_score_gemma":0.003192591,"domain_scores_codex":[0.9996917,0.00008646149,0.000009199455,0.00007798119,0.00007720966,0.00005745807],"domain_scores_gemma":[0.9993692,0.0003448699,0.00006121722,0.0001139192,0.00007162126,0.00003921408],"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":[0.0001943286,0.00004998684,0.0007689124,0.00005510353,0.00006663217,0.0001401906,0.00006433009,0.9047945,0.004446746,0.01536353,0.003542513,0.07051334],"study_design_scores_gemma":[0.000002804224,0.00001128391,0.00005892089,0.000004860085,0.000003269098,0.0000147332,0.000003022528,0.9960409,0.0008063542,0.002593082,0.0004570737,0.000003672112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0781597,0.001105619,0.900462,0.001821576,0.0004593449,0.0001126104,0.0003722049,0.003826583,0.01368027],"genre_scores_gemma":[0.9187413,0.0003508264,0.06652337,0.0004027557,0.00009608749,0.00007783198,0.0003388721,0.0001774018,0.01329154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004831031,"threshold_uncertainty_score":0.01616144,"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."}}