{"id":"W1513430854","doi":"10.1109/iscas.2015.7168817","title":"Optimum multiplicative watermark detector in contourlet domain using the normal inverse Gaussian distribution","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Watermark; Contourlet; Generalized normal distribution; Detector; Digital watermarking; Gaussian noise; Gaussian; Constant false alarm rate; Computer science; Inverse Gaussian distribution; Algorithm; Additive white Gaussian noise; Artificial intelligence; False alarm; Noise (video); Mathematics; Computer vision; Normal distribution; Statistics; Distribution (mathematics); Wavelet; Image (mathematics); Physics; Telecommunications; Wavelet transform; Mathematical analysis; White noise","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":[0.001342258,0.0005028385,0.0007765145,0.0005536249,0.0002394623,0.0006699542,0.0006503466,0.0009802426,0.0004588792],"category_scores_gemma":[0.003544669,0.0002947761,0.0004795966,0.0006518193,0.0008271537,0.001451924,0.0005465153,0.0006613598,0.0003011473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004837165,"about_ca_system_score_gemma":0.0006792921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005596664,"about_ca_topic_score_gemma":0.0005684277,"domain_scores_codex":[0.9988924,0.000268906,0.0000469386,0.0002342742,0.0004981902,0.00005938979],"domain_scores_gemma":[0.9986463,0.0007424193,0.0001543362,0.0001140096,0.0003133409,0.00002967477],"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":[0.0009592554,0.0002386246,0.003669488,0.0003309381,0.0001479423,0.0003355561,0.0002084588,0.1845344,0.2972218,0.05302271,0.001458141,0.4578727],"study_design_scores_gemma":[0.00002843119,0.000142457,0.0006343757,0.00001078348,0.00002600023,0.0004125389,0.00001352543,0.9429743,0.04957315,0.005210062,0.000946817,0.00002749993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0219464,0.0001932684,0.9769992,0.00007635233,0.00001664303,0.0000177164,0.00001322012,0.0001406705,0.0005964372],"genre_scores_gemma":[0.5713282,0.0005753417,0.4261653,0.0001029414,0.00004613718,0.0000494797,0.00007880487,0.0000384959,0.001615347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001342258,"threshold_uncertainty_score":0.007098615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834008065142246,"score_gpt":0.2702937955263272,"score_spread":0.2419537148749047,"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."}}