{"id":"W2800157031","doi":"10.1139/tcsme-2013-0035","title":"A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK","year":2013,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Council","keywords":"Digital watermarking; Watermark; Salt-and-pepper noise; Noise (video); Computer science; Gaussian noise; Code (set theory); Hadamard transform; Algorithm; Filter (signal processing); Median filter; Artificial neural network; Artificial intelligence; Computer vision; Mathematics; Image (mathematics); Image processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005589661,0.0003165576,0.0003044469,0.0002882066,0.0002447153,0.000323422,0.0003665165,0.0006178383,0.0004948534],"category_scores_gemma":[0.001980609,0.0001923973,0.0003712798,0.0003307362,0.0004763272,0.001066223,0.0001654337,0.0003804171,0.00007471245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004093922,"about_ca_system_score_gemma":0.0002865753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790218,"about_ca_topic_score_gemma":0.001728555,"domain_scores_codex":[0.9997523,0.00005795543,0.00001318271,0.00005965205,0.00009505945,0.0000217661],"domain_scores_gemma":[0.999364,0.0003654016,0.0000461796,0.00003278111,0.0001763239,0.00001531726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004722162,0.0003995206,0.006709247,0.0003628826,0.0002128979,0.0005518124,0.0003766193,0.5664069,0.1389867,0.01896243,0.0005852724,0.2659736],"study_design_scores_gemma":[0.000003183626,0.00006895213,0.0004694698,0.000002508823,0.00001090714,0.00003425231,0.000007438852,0.9933845,0.005528194,0.0003473497,0.0001384223,0.000004755565],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5943047,0.001795679,0.3985487,0.0003532562,0.0001271782,0.00006763728,0.00001505275,0.0001388878,0.004648949],"genre_scores_gemma":[0.9545702,0.0006783904,0.04202442,0.00002950449,0.00004242274,0.00002104699,0.00001696901,0.00001172699,0.002605335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003790218,"threshold_uncertainty_score":0.007536352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02547028903528914,"score_gpt":0.2193379989050371,"score_spread":0.1938677098697479,"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."}}