{"id":"W2156953164","doi":"10.1155/2014/580697","title":"An Improvement Technique Based on Structural Similarity Thresholding for Digital Watermarking","year":2014,"lang":"en","type":"article","venue":"Advances in Computer Engineering","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Digital watermarking; Discrete cosine transform; Thresholding; Watermark; Artificial intelligence; Robustness (evolution); Embedding; Computer science; Block (permutation group theory); Image (mathematics); Similarity (geometry); Computer vision; Authentication (law); Pattern recognition (psychology); Mathematics; Algorithm; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003032811,0.0002717723,0.0002316399,0.0002962737,0.0001007086,0.000242015,0.0008587251,0.00007370248,3.39632e-7],"category_scores_gemma":[0.00001486749,0.0002536658,0.00008738469,0.0002370323,0.00002179435,0.001721583,0.000135414,0.0002100429,2.418093e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006669516,"about_ca_system_score_gemma":0.000007716135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.974333e-7,"about_ca_topic_score_gemma":7.005811e-7,"domain_scores_codex":[0.9985092,0.00001810356,0.0002962514,0.0005436261,0.0001828466,0.0004499199],"domain_scores_gemma":[0.999033,0.0001841513,0.00007260093,0.0005965739,0.00003717498,0.00007652159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000156177,0.00003328206,0.001229748,0.00009739918,0.000003785701,0.000005285755,0.00005504815,0.7046478,0.001807993,0.006796392,0.000002474082,0.2853052],"study_design_scores_gemma":[0.0003000806,0.0004083944,0.0002153602,0.000163207,0.00000127385,0.000003522597,5.769464e-7,0.9595267,0.02607702,0.0106238,0.002340029,0.0003400318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005639825,0.00002756151,0.9926438,0.0000407909,0.0004336121,0.0004417667,0.000005708346,0.0007183641,0.00004852809],"genre_scores_gemma":[0.5746768,0.000002788548,0.4249925,0.0001141642,0.00009742685,0.00009398926,0.000007397517,0.00001476685,2.482151e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5690369,"threshold_uncertainty_score":0.9999915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005513709564222702,"score_gpt":0.2416104295205663,"score_spread":0.2360967199563436,"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."}}