{"id":"W1993395332","doi":"10.1016/j.eswa.2007.12.046","title":"Image watermarking scheme using nonnegative matrix factorization and wavelet transform","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-negative matrix factorization; Digital watermarking; Discrete wavelet transform; Matrix decomposition; Lifting scheme; Wavelet; Wavelet packet decomposition; Wavelet transform; Mathematics; Image (mathematics); Stationary wavelet transform; Factorization; Second-generation wavelet transform; Artificial intelligence; Scheme (mathematics); Pattern recognition (psychology); Algorithm; Eigendecomposition of a matrix; Matrix (chemical analysis); Distortion (music); Computer science; Eigenvalues and eigenvectors","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008422443,0.000179669,0.0001831133,0.0001388384,0.0005746108,0.0001052937,0.0003102067,0.00006478717,7.747221e-7],"category_scores_gemma":[0.000002072824,0.000141377,0.00003229076,0.0004114817,0.0001182899,0.0008141373,0.00005109344,0.00009540663,0.0000021764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000474567,"about_ca_system_score_gemma":0.00003195088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007584223,"about_ca_topic_score_gemma":0.000001455042,"domain_scores_codex":[0.9989133,0.00003493217,0.0002412945,0.0003806678,0.0001934761,0.0002362788],"domain_scores_gemma":[0.9992415,0.00003588351,0.0001153278,0.0004098675,0.0001120979,0.00008529609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001338266,0.0006443561,0.0100251,0.0007130654,0.0004160489,0.0001847552,0.07090434,0.0002407795,0.6479257,0.2456624,0.0008948771,0.02225472],"study_design_scores_gemma":[0.003344421,0.0005404315,0.002707545,0.0009583958,0.00004989966,0.00468118,0.001797164,0.667224,0.168553,0.009457609,0.136859,0.003827242],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005426718,0.0003096648,0.9924336,0.0001159576,0.00004088539,0.0008110422,0.000006693325,0.0004079864,0.0004474148],"genre_scores_gemma":[0.5906533,0.00008013195,0.4087278,0.00002171115,0.00006375182,0.0003944979,0.000009025124,0.00001403686,0.00003569708],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6669832,"threshold_uncertainty_score":0.5765188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859545231268303,"score_gpt":0.2694904914033506,"score_spread":0.2508950390906676,"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."}}