{"id":"W4206780176","doi":"10.18280/ts.380607","title":"An Improved Medical Image Watermarking Technique Based on Weber’s Law Descriptors","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital watermarking; Discrete cosine transform; Watermark; Robustness (evolution); Embedding; Chaotic; Block (permutation group theory); Artificial intelligence; Computer vision; Computer science; Image (mathematics); Pixel; Algorithm; Pattern recognition (psychology); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008007751,0.0002967074,0.0002537505,0.0001612005,0.0002894539,0.0002873576,0.001115052,0.0001646032,0.0001997115],"category_scores_gemma":[0.00001617525,0.000261174,0.0001653905,0.0003572766,0.0001421638,0.0007576417,0.0001633702,0.0003547406,0.000005300773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006656667,"about_ca_system_score_gemma":0.0001254004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003031659,"about_ca_topic_score_gemma":0.00002134988,"domain_scores_codex":[0.9974148,0.0002759301,0.0004077327,0.0006943154,0.0006884027,0.0005188431],"domain_scores_gemma":[0.9986867,0.00008228594,0.00009851929,0.0007312252,0.0001249463,0.0002763369],"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":[0.0001671908,0.001766242,0.0006142708,0.0001388686,0.00007602575,0.001652715,0.0006764639,0.0001147607,0.7986803,0.09327506,0.001079811,0.1017583],"study_design_scores_gemma":[0.0008231495,0.0005921802,0.0002916186,0.000226382,0.00001367283,0.00005833366,0.00001649278,0.1527254,0.8272585,0.007648371,0.009766101,0.000579841],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003304428,0.000016239,0.9916165,0.0009541381,0.0001915413,0.0003266954,0.000007533391,0.0009339108,0.002649035],"genre_scores_gemma":[0.7913215,0.000005293783,0.2055145,0.002846061,0.0001052547,0.0001433186,0.00002848708,0.00002202588,0.00001354046],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7880171,"threshold_uncertainty_score":0.999984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105413132490904,"score_gpt":0.2498608666672426,"score_spread":0.2388067353423335,"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."}}