{"id":"W4311164555","doi":"10.18280/ts.390533","title":"Entropy Based Secure and Robust Image Watermarking Using Lifting Wavelet Transform and Multi-Level-Multiple Image Scrambling Technique","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scrambling; Digital watermarking; Computer science; Artificial intelligence; Entropy (arrow of time); Robustness (evolution); Computer vision; Fidelity; Watermark; Image (mathematics); Pattern recognition (psychology); Algorithm; Telecommunications","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":[0.0009560412,0.000352638,0.0003047987,0.0003679373,0.001118227,0.0003459529,0.0005298234,0.00006872221,0.00002826194],"category_scores_gemma":[0.00001163499,0.0003484593,0.0001093382,0.0003898533,0.0001347085,0.0009435167,0.0004112403,0.0004438694,2.174982e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009575065,"about_ca_system_score_gemma":0.00004468408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003351589,"about_ca_topic_score_gemma":0.000004551301,"domain_scores_codex":[0.9976583,0.0001917152,0.0004641473,0.0006800038,0.0004141361,0.000591733],"domain_scores_gemma":[0.9992262,0.0001145136,0.0001744079,0.0002766017,0.0000694362,0.0001388981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001304561,0.0002905071,0.002101189,0.0002603974,0.00007189997,0.0002464906,0.002817954,0.001594185,0.9443038,0.001228936,0.00004539541,0.04690877],"study_design_scores_gemma":[0.001617045,0.0002141045,0.0004968276,0.000127864,0.00003390008,0.0001280711,0.0001828375,0.8159409,0.1789775,0.001074709,0.000610363,0.0005957959],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0669818,0.00008901845,0.9312499,0.0003009423,0.00006189574,0.0007813756,0.00005450119,0.0004395944,0.00004095009],"genre_scores_gemma":[0.5169205,0.00000932487,0.4827389,0.0001650274,0.00002868537,0.00009977398,0.00001396138,0.00002106111,0.000002758451],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8143467,"threshold_uncertainty_score":0.9998968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03577478454138421,"score_gpt":0.2502137295827747,"score_spread":0.2144389450413904,"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."}}