{"id":"W2168572315","doi":"10.1109/tip.2003.821552","title":"Dual Domain Watermarking for Authentication and Compression of Cultural Heritage Images","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Digital watermarking; Watermark; Chrominance; Computer science; Computer vision; Authentication (law); Discrete cosine transform; Artificial intelligence; Orthogonality; Lossy compression; Data compression; Image (mathematics); Luminance; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001374419,0.0001378663,0.0001442885,0.0001350397,0.0003689715,0.0001567468,0.0001928417,0.00005140804,6.431913e-7],"category_scores_gemma":[0.000002363985,0.0001151033,0.00006721133,0.0001802461,0.0001423934,0.00126151,0.000004601943,0.0001101224,3.236418e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002301555,"about_ca_system_score_gemma":0.0000204508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004887186,"about_ca_topic_score_gemma":9.801872e-7,"domain_scores_codex":[0.9991464,0.00002409189,0.0002263345,0.0002798697,0.0001347677,0.0001885218],"domain_scores_gemma":[0.9995232,0.00003230578,0.0001120314,0.0001798403,0.0001092451,0.00004342704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007154413,0.0001908084,0.000009659781,0.0003565485,0.00001730595,0.000005191234,0.006111565,0.0004723402,0.841968,0.0005172155,0.000007972648,0.1502718],"study_design_scores_gemma":[0.0006549195,0.0001074123,0.00009856303,0.0003700193,0.00001563817,0.00003058408,0.0001934383,0.003738266,0.9791115,0.01543545,0.00005190127,0.0001922996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04614507,0.00008493153,0.9529671,0.0002214171,0.00008238107,0.0002144664,0.00000905745,0.0002223533,0.000053217],"genre_scores_gemma":[0.6330796,0.00001433987,0.366823,0.00002097461,0.000009390556,0.0000341589,0.000001173324,0.000007257291,0.00001016808],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5869345,"threshold_uncertainty_score":0.4693776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01466559939382253,"score_gpt":0.2765797237414289,"score_spread":0.2619141243476064,"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."}}