{"id":"W2162478380","doi":"10.1109/icme.2006.262786","title":"Minimum Distortion Look-Up Table Based Data Hiding","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Lookup table; Robustness (evolution); Embedding; Distortion (music); Computer science; Algorithm; Table (database); Viterbi algorithm; Information hiding; Artificial intelligence; Telecommunications; Decoding methods; Data mining","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":[0.0001985828,0.0003459059,0.0004711073,0.0003588553,0.0001949023,0.0005017634,0.0007659263,0.0004147943,0.001796719],"category_scores_gemma":[0.0007172399,0.0001722172,0.0002900361,0.0003585906,0.0002686088,0.001067723,0.0002997426,0.0004068105,0.0006276922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003112131,"about_ca_system_score_gemma":0.0002323556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003154005,"about_ca_topic_score_gemma":0.0005602871,"domain_scores_codex":[0.9997258,0.00003681171,0.00001810641,0.0000337696,0.0001632682,0.00002231172],"domain_scores_gemma":[0.9996462,0.0001253423,0.00006153562,0.00006886479,0.00008650638,0.00001159333],"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":[0.000827823,0.00008944751,0.001150241,0.0007846914,0.0001328788,0.0004730456,0.0001481392,0.07752498,0.5247443,0.04249812,0.002774224,0.3488521],"study_design_scores_gemma":[0.00007973807,0.000472874,0.0007935335,0.00003719829,0.0001026931,0.001460391,0.00002554356,0.569057,0.4109786,0.005212484,0.01170091,0.00007896882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04304413,0.001412197,0.9518127,0.0001445258,0.0001042785,0.00003620129,0.00008996089,0.0008239973,0.002531986],"genre_scores_gemma":[0.6581645,0.0008803678,0.3361519,0.0001347545,0.0000818652,0.0000316479,0.0001697593,0.00007719984,0.004307993],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001796719,"threshold_uncertainty_score":0.006010652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02670260604939024,"score_gpt":0.2571079516489768,"score_spread":0.2304053455995865,"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."}}