{"id":"W2293936600","doi":"10.1109/tmm.2015.2508147","title":"Multiplicative Watermark Decoder in Contourlet Domain Using the Normal Inverse Gaussian Distribution","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Contourlet; Watermark; Generalized normal distribution; Digital watermarking; Computer science; Robustness (evolution); Artificial intelligence; Pattern recognition (psychology); Gaussian; Algorithm; Computer vision; Normal distribution; Mathematics; Image (mathematics); Wavelet; Statistics; Wavelet transform","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":[0.0004097328,0.0002026323,0.0001702594,0.0001495975,0.0002219131,0.00007116734,0.0005761379,0.0001123326,0.00000388838],"category_scores_gemma":[0.000008910857,0.0001481434,0.00009791543,0.0004807302,0.0001764506,0.000651857,0.000007950877,0.0003719361,0.00001400603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001683806,"about_ca_system_score_gemma":0.00005747521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001754057,"about_ca_topic_score_gemma":0.0002256075,"domain_scores_codex":[0.9985439,0.0002097379,0.0002797986,0.0003487165,0.0002532231,0.0003646284],"domain_scores_gemma":[0.9990211,0.0001380643,0.00008921664,0.000523322,0.0000781123,0.0001501871],"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":[0.002198271,0.004679813,0.009666594,0.0001188125,0.0005408833,0.0006159734,0.1370141,0.225656,0.05687919,0.004110615,0.004437178,0.5540825],"study_design_scores_gemma":[0.003016057,0.0001933085,0.001803423,0.0001121227,0.0000266895,0.00006113066,0.0005740764,0.8470613,0.1384337,0.004481769,0.003523088,0.0007133419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0477343,0.00001167333,0.9507098,0.000446434,0.0004012298,0.0003621851,0.00004061963,0.0002080528,0.00008574827],"genre_scores_gemma":[0.8758514,0.00001015465,0.1238248,0.0001572254,0.00003127637,0.00008179814,0.000008576444,0.00001089799,0.00002381251],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8281171,"threshold_uncertainty_score":0.6041114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03100630872646192,"score_gpt":0.2782674574003173,"score_spread":0.2472611486738553,"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."}}