{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008495164,0.0004937447,0.001338884,0.00076199,0.0003044295,0.0006498153,0.001030336,0.001363743,0.000956713],"category_scores_gemma":[0.001512176,0.0004442349,0.0006808703,0.0005825649,0.0004976758,0.001620424,0.0007019372,0.0009656132,0.0006329088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004109101,"about_ca_system_score_gemma":0.0008843897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008617789,"about_ca_topic_score_gemma":0.001139302,"domain_scores_codex":[0.9993535,0.00009332994,0.00003533814,0.000170446,0.0002952513,0.00005211404],"domain_scores_gemma":[0.9994197,0.0002353319,0.00007229873,0.00006252406,0.0001748141,0.00003543471],"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":[0.0006726907,0.0002677908,0.002925738,0.0004476022,0.0002695176,0.0004191502,0.00009795411,0.08491524,0.2586597,0.02366491,0.003775404,0.6238844],"study_design_scores_gemma":[0.00003710255,0.0001559686,0.0006881258,0.00001240349,0.00005090306,0.0004543612,0.000007440669,0.9517348,0.04070937,0.003277679,0.002822893,0.00004890101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007740736,0.0004017815,0.9908128,0.00005832258,0.00007272012,0.0000250916,0.00003674558,0.0004909293,0.0003608946],"genre_scores_gemma":[0.3111423,0.0009679761,0.6802661,0.0002472754,0.0001184048,0.0001045727,0.0002815601,0.00007526074,0.006796636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001363743,"threshold_uncertainty_score":0.0044927,"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."}}