{"id":"W4313400654","doi":"10.1007/s11071-022-08206-8","title":"Low-cost multiclass-image encryption based on compressive sensing and chaotic system","year":2022,"lang":"en","type":"article","venue":"Nonlinear Dynamics","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Encryption; Computer science; Scrambling; Key space; Chaotic; Grayscale; Compressed sensing; Cryptography; Computer engineering; Computer vision; Artificial intelligence; Algorithm; Real-time computing; Image (mathematics); Computer network","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":[0.0001699218,0.0003293294,0.0003754853,0.0001873857,0.0003009912,0.0003534207,0.0003909963,0.0004291538,0.001827845],"category_scores_gemma":[0.0004338291,0.0001135029,0.0001833521,0.0002575075,0.0002431431,0.000781445,0.0004195837,0.000416432,0.0003350498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003066863,"about_ca_system_score_gemma":0.0003053973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004445297,"about_ca_topic_score_gemma":0.000977125,"domain_scores_codex":[0.9997793,0.00003685566,0.000009333121,0.00003427236,0.0001186268,0.00002174179],"domain_scores_gemma":[0.9998216,0.00005915943,0.0000345773,0.00002801274,0.00004219411,0.0000145274],"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":[0.001086822,0.0002389609,0.002184203,0.0003932819,0.00009826278,0.0004677535,0.0001450007,0.06520706,0.6328437,0.05083109,0.004240882,0.242263],"study_design_scores_gemma":[0.0000366486,0.000210142,0.0009380789,0.00002039158,0.00002579545,0.0005739622,0.00002433773,0.9138203,0.07744248,0.003674831,0.00319952,0.00003349477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.309661,0.001855006,0.6662776,0.001380119,0.0003105174,0.0001423541,0.0002268095,0.0004374055,0.01970921],"genre_scores_gemma":[0.9037012,0.0003479348,0.0916367,0.00009955236,0.00008208588,0.00004825294,0.00008084593,0.00001257254,0.00399096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001827845,"threshold_uncertainty_score":0.006114721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00821209856044802,"score_gpt":0.2252928831347134,"score_spread":0.2170807845742653,"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."}}