{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002366817,0.0003649392,0.0003486648,0.0009343678,0.0002137202,0.0003567026,0.0004704224,0.0004298476,0.001082987],"category_scores_gemma":[0.000686144,0.0001369836,0.0004066105,0.000823815,0.0004373504,0.001410635,0.0003803507,0.0004510579,0.0003907997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650521,"about_ca_system_score_gemma":0.0004660564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006009827,"about_ca_topic_score_gemma":0.0004907937,"domain_scores_codex":[0.9997879,0.00002539607,0.0000167878,0.00004086929,0.0001130282,0.0000160569],"domain_scores_gemma":[0.9998281,0.00004562801,0.00003047826,0.00002891703,0.00005662869,0.00001023015],"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":[0.0002398022,0.00008345542,0.0006763348,0.0003565523,0.0000358695,0.0003393536,0.0001117987,0.0157528,0.471293,0.05328077,0.0023048,0.4555254],"study_design_scores_gemma":[0.0001219752,0.0005916986,0.00224205,0.00005183703,0.00009051822,0.00277334,0.00006317353,0.5822001,0.3607584,0.01211261,0.03885742,0.0001369681],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05256769,0.001947883,0.9400648,0.0003106164,0.000250033,0.00009033695,0.0000686792,0.0005025212,0.004197451],"genre_scores_gemma":[0.5714017,0.002691117,0.4144449,0.0001550772,0.000240389,0.0000918993,0.0001815926,0.00007175249,0.01072149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001082987,"threshold_uncertainty_score":0.003622949,"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."}}