{"id":"W2036949513","doi":"10.1109/iscas.2014.6865627","title":"A new blind wavelet domain watermark detector using hidden Markov model","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Wavelet; Hidden Markov model; Watermark; Pattern recognition (psychology); Detector; Computer science; Wavelet transform; Artificial intelligence; Marginal distribution; Scale (ratio); Markov chain; Markov process; Stationary wavelet transform; Algorithm; Discrete wavelet transform; Mathematics; Image (mathematics); Statistics; Physics; Machine learning; Random variable","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003460799,0.0002360597,0.000216614,0.0002133887,0.0001837719,0.0001902025,0.001068108,0.0001116768,0.00001499065],"category_scores_gemma":[0.00001141166,0.0001896916,0.0001260022,0.0003120265,0.00003960983,0.0006964659,0.0004043143,0.0001367638,0.000008044824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003505201,"about_ca_system_score_gemma":0.00005098966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003636301,"about_ca_topic_score_gemma":0.000009806643,"domain_scores_codex":[0.9984165,0.00008397033,0.0002736884,0.0004886755,0.0002636771,0.0004735246],"domain_scores_gemma":[0.9988239,0.00004984229,0.00008540592,0.0008066825,0.00004564533,0.0001884791],"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":[0.0001430639,0.00009149288,0.0006764847,0.00006076526,0.00006971008,0.00005213301,0.001550485,0.0003760943,0.06497826,0.06978664,0.004601107,0.8576137],"study_design_scores_gemma":[0.0006591554,0.00007693919,0.00007658979,0.00003925761,0.000007959219,0.0000570605,0.000004621527,0.7314169,0.04408289,0.2204954,0.00259945,0.0004836633],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03311259,0.00001443671,0.9611791,0.0002481531,0.0001266664,0.0001685921,0.000001039948,0.0007101544,0.004439267],"genre_scores_gemma":[0.2764046,0.000003618792,0.7227139,0.0003435874,0.00007775736,0.000005679741,0.000001102768,0.00001433047,0.0004354496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8571301,"threshold_uncertainty_score":0.7735398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01951076960718134,"score_gpt":0.2547432594256691,"score_spread":0.2352324898184877,"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."}}