{"id":"W2096341257","doi":"10.1109/ccece.2002.1013058","title":"Wavelet-based digital watermarking for image authentication","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Digital watermarking; Computer science; Computer vision; Wavelet; Quantization (signal processing); Wavelet packet decomposition; Transparency (behavior); Human visual system model; Digital image; Wavelet transform; Image (mathematics); Pattern recognition (psychology); Image processing; Computer security","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":[0.0003253778,0.0004175659,0.0003781749,0.0005964146,0.0002619747,0.0005903173,0.0005922249,0.0008446368,0.003238515],"category_scores_gemma":[0.0009159414,0.000160521,0.0003131396,0.0008703794,0.0005548339,0.001477736,0.0005648407,0.0006778535,0.001926536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002473963,"about_ca_system_score_gemma":0.0002181967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001417628,"about_ca_topic_score_gemma":0.0001954069,"domain_scores_codex":[0.9997403,0.00005019047,0.0000142613,0.00002921671,0.0001448989,0.00002107393],"domain_scores_gemma":[0.9997347,0.0000782464,0.00004119569,0.00006938318,0.00006396147,0.00001236885],"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":[0.0002429043,0.00009716622,0.0003333003,0.0006019185,0.00005663074,0.0003967614,0.00009779578,0.01713799,0.3218158,0.1187276,0.005432459,0.5350597],"study_design_scores_gemma":[0.00008917371,0.0007192739,0.001130342,0.0001889729,0.0001267501,0.002024326,0.00006789499,0.5259429,0.2729818,0.06977555,0.1268428,0.0001102157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01798679,0.007773853,0.9647827,0.0006341101,0.0003786383,0.0000788875,0.00007560743,0.0007347133,0.007554682],"genre_scores_gemma":[0.3364537,0.01289166,0.6325833,0.0002557973,0.0004527027,0.0001047758,0.0002726739,0.0001124025,0.01687296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003238515,"threshold_uncertainty_score":0.01083398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01238360990111112,"score_gpt":0.2472620929088511,"score_spread":0.23487848300774,"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."}}