{"id":"W2019724581","doi":"10.1109/tip.2013.2246177","title":"Locally Optimal Detection of Image Watermarks in the Wavelet Domain Using Bessel K Form Distribution","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Natural Resources Canada","funders":"","keywords":"Detector; Watermark; Gaussian noise; Mathematics; Probability density function; Wavelet; Algorithm; Noise (video); Generalized normal distribution; Bessel function; Computer science; Artificial intelligence; Image (mathematics); Statistics; Normal distribution; Mathematical analysis; Telecommunications","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":[0.001492619,0.0005020407,0.0009913207,0.0006624022,0.0001904625,0.0005787216,0.0004725967,0.0008943421,0.0003390833],"category_scores_gemma":[0.004386954,0.0002932081,0.0005247006,0.0005240203,0.0008150149,0.001619552,0.0007288699,0.0005354109,0.0002611383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003561419,"about_ca_system_score_gemma":0.0003934565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003606992,"about_ca_topic_score_gemma":0.0003834027,"domain_scores_codex":[0.9992245,0.0002301268,0.00004459229,0.0001643993,0.0002809418,0.00005537937],"domain_scores_gemma":[0.9986269,0.0007962677,0.0001999501,0.000134214,0.000206045,0.00003656211],"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":[0.0008328904,0.0001567185,0.004416635,0.000340338,0.0001602711,0.0004965612,0.0002437478,0.1758551,0.3850937,0.04173948,0.0008791473,0.3897854],"study_design_scores_gemma":[0.00002227313,0.0001600898,0.001355381,0.00001179118,0.00002703214,0.0004558464,0.00001817495,0.9296719,0.06159501,0.006023899,0.0006119582,0.00004661883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05426438,0.0002948127,0.9446922,0.00006205168,0.00001224,0.0000149926,0.00001508634,0.0001986842,0.0004455905],"genre_scores_gemma":[0.7074271,0.0006531486,0.290169,0.0001049366,0.00004477747,0.00003593472,0.00006774305,0.00005195541,0.001445484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001492619,"threshold_uncertainty_score":0.007893801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009904236977774287,"score_gpt":0.2453926215216164,"score_spread":0.2354883845438422,"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."}}