{"id":"W4363674634","doi":"10.3390/cryptography7020020","title":"Protecting Digital Images Using Keys Enhanced by 2D Chaotic Logistic Maps","year":2023,"lang":"en","type":"article","venue":"Cryptography","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Encryption; Computer science; Chaotic; Cryptography; Robustness (evolution); Key (lock); Digital image; Color image; Image (mathematics); Computer vision; Artificial intelligence; Data mining; Algorithm; Image processing; Computer security","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":[0.0002516482,0.0003809297,0.0003379702,0.0005707238,0.0003593043,0.0006337204,0.0004035267,0.0004945014,0.001369604],"category_scores_gemma":[0.0007506809,0.0002024672,0.0004424217,0.0004337801,0.0003715411,0.001612863,0.0009052689,0.0003630935,0.0005980891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000312982,"about_ca_system_score_gemma":0.0003569586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004196371,"about_ca_topic_score_gemma":0.0003877891,"domain_scores_codex":[0.9997397,0.00004613938,0.00002111629,0.00004126317,0.0001146688,0.00003710417],"domain_scores_gemma":[0.9997802,0.00005417193,0.00005077822,0.00005029041,0.00005159587,0.00001290447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008277128,0.0001111645,0.002204335,0.0005404035,0.0001200714,0.001288192,0.0005582209,0.07937585,0.6415114,0.04774352,0.002161955,0.2235573],"study_design_scores_gemma":[0.0001199641,0.0005401931,0.002335304,0.00005598896,0.00009692225,0.002508417,0.0001963774,0.630036,0.3251168,0.01456022,0.02425572,0.0001780366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3055874,0.00156025,0.6785336,0.0005551028,0.0002280541,0.0001529743,0.0001593042,0.001152395,0.01207107],"genre_scores_gemma":[0.9114481,0.0006912212,0.08209586,0.00006894807,0.00003901515,0.00006214515,0.00005615985,0.0000445612,0.005494017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001369604,"threshold_uncertainty_score":0.004581749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02911554176635654,"score_gpt":0.266456921556084,"score_spread":0.2373413797897275,"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."}}