{"id":"W2146713890","doi":"10.1109/bsc.2006.1644638","title":"Secure Copyright Protection of Digital Images Using Nonnegative Matrix Factorization","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Digital watermarking; Robustness (evolution); Singular value decomposition; Matrix decomposition; Computer science; Embedding; Factorization; Non-negative matrix factorization; Digital image; Matrix (chemical analysis); Computer vision; Algorithm; Artificial intelligence; Image (mathematics); Theoretical computer science; Image processing","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":[0.00004207075,0.00008848433,0.0000917612,0.0001184199,0.00007663196,0.00009367907,0.0001896616,0.00004539331,0.000002946299],"category_scores_gemma":[0.000005714651,0.00007097491,0.00004721741,0.0003651805,0.00004100179,0.00129446,0.00005888985,0.0000541836,8.790373e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002228815,"about_ca_system_score_gemma":0.00001359418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004878384,"about_ca_topic_score_gemma":0.000001778107,"domain_scores_codex":[0.9994043,0.00001745195,0.0001636083,0.0001702573,0.0001329495,0.0001114558],"domain_scores_gemma":[0.9995896,0.00001520256,0.000115523,0.0001643497,0.0001006454,0.00001471307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006713057,0.0004931362,0.0173116,0.0002039697,0.00006468017,0.00002076684,0.001346863,0.002391969,0.4449676,0.5096706,0.0006998212,0.0227619],"study_design_scores_gemma":[0.000131279,0.00008102303,0.0008252929,0.00003076483,0.000003670438,0.000009236571,0.000009763778,0.0199331,0.8209312,0.1575359,0.0003399479,0.000168825],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01882673,0.00001399616,0.9786488,0.00003413697,0.00006209456,0.0001841897,0.000006269204,0.0002947123,0.001929019],"genre_scores_gemma":[0.859933,9.789203e-7,0.1399159,0.000002728873,0.00002748762,0.000004467658,0.000003779713,0.000004286064,0.0001073713],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8411062,"threshold_uncertainty_score":0.2894273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.011753805487773,"score_gpt":0.2497523670591389,"score_spread":0.237998561571366,"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."}}