{"id":"W4367281068","doi":"10.33103/uot.ijccce.22.3.5","title":"RCAE_BFV: Retrieve Encrypted Images using Convolution AutoEncoder and BFV","year":2022,"lang":"en","type":"article","venue":"Iraqi Journal of Computer Communication Control and System Engineering","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Homomorphic encryption; Autoencoder; Encryption; Image retrieval; Artificial intelligence; Deep learning; Computer vision; Image (mathematics); Pattern recognition (psychology); Information retrieval; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003653999,0.0004954058,0.0004760426,0.0004802157,0.0002641658,0.0004528336,0.0006201179,0.0006418992,0.003125019],"category_scores_gemma":[0.000822223,0.0001635679,0.0004426769,0.0003427099,0.0002692362,0.001076056,0.0006016277,0.0005593075,0.001345789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003903599,"about_ca_system_score_gemma":0.0005114702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002792262,"about_ca_topic_score_gemma":0.002714625,"domain_scores_codex":[0.9996665,0.00003113906,0.00002606599,0.00005553262,0.0001771394,0.00004371919],"domain_scores_gemma":[0.9998312,0.0000298439,0.00001938731,0.00005642993,0.00005419728,0.000008833998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006681521,0.0002626515,0.001779332,0.0002104232,0.0001164174,0.000397439,0.0001188638,0.06529091,0.1637449,0.009325558,0.01004795,0.7480375],"study_design_scores_gemma":[0.00005648217,0.0002692605,0.001652909,0.00002810828,0.00002670778,0.0009190082,0.00004122617,0.8276958,0.1548803,0.003490142,0.0109012,0.0000388234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07514854,0.0009276898,0.9117602,0.0002878549,0.0001730018,0.000209046,0.0004662611,0.004680003,0.006347474],"genre_scores_gemma":[0.560285,0.0006556141,0.4204045,0.0002518907,0.00005901472,0.0001846629,0.001431833,0.0001848567,0.01654256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003125019,"threshold_uncertainty_score":0.01045418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008953874548216317,"score_gpt":0.1998921013640104,"score_spread":0.1909382268157941,"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."}}