{"id":"W2168792047","doi":"10.1109/icip.2009.5414115","title":"Minimum mean square error detector for multimessage spread spectrum embedding","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Detector; Minimum mean square error; Digital watermarking; Spread spectrum; Computer science; Mean squared error; Bit error rate; Embedding; Algorithm; Mathematics; Statistics; Telecommunications; Artificial intelligence; Decoding methods; Code division multiple access; Image (mathematics)","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":[0.001381771,0.0004891502,0.0007663359,0.0006482836,0.0002735581,0.0006067895,0.000697936,0.001292683,0.001348212],"category_scores_gemma":[0.004115353,0.0002701119,0.000319714,0.0004207412,0.0004041937,0.001125144,0.0006112882,0.000978935,0.0008200833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003372218,"about_ca_system_score_gemma":0.0004208439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002940948,"about_ca_topic_score_gemma":0.0005048012,"domain_scores_codex":[0.9987398,0.0002847519,0.00007248191,0.000159928,0.0006958871,0.00004714918],"domain_scores_gemma":[0.9984005,0.0007442857,0.0001363803,0.0001653784,0.0005171752,0.00003624999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00110182,0.0002160177,0.00260658,0.0004932635,0.0002528016,0.0003764733,0.0001430269,0.08755606,0.2890173,0.03677266,0.004234612,0.5772294],"study_design_scores_gemma":[0.00005841446,0.0004281017,0.00108785,0.00004737857,0.00006955311,0.000703692,0.00002347374,0.8457043,0.1355916,0.006217337,0.01000133,0.00006699507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01163961,0.001092236,0.9857407,0.0001648119,0.0001152348,0.00002564104,0.00002815701,0.0002946388,0.0008988886],"genre_scores_gemma":[0.416444,0.001274759,0.5756119,0.0003103146,0.0001991522,0.0001040228,0.000186668,0.00005325118,0.005815855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001381771,"threshold_uncertainty_score":0.007307589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01892149789990048,"score_gpt":0.2900241268444244,"score_spread":0.2711026289445239,"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."}}