{"id":"W2369433650","doi":"","title":"A Robust Watermarking Algorithm Based on RadonTransform and Multiple-Level Discrete Cosine Transform","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discrete cosine transform; Digital watermarking; Contourlet; Computer science; Discrete wavelet transform; Radon transform; Modified discrete cosine transform; Algorithm; Watermark; Lapped transform; Discrete sine transform; Robustness (evolution); Discrete Fourier transform (general); Transform coding; Discrete Hartley transform; Wavelet transform; Artificial intelligence; Mathematics; Image (mathematics); Fractional Fourier transform; Wavelet; Fourier transform","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":[],"consensus_categories":[],"category_scores_codex":[0.0001328125,0.00018746,0.0001442405,0.0001095453,0.0001654502,0.0000500311,0.000114193,0.00008280978,0.00001343146],"category_scores_gemma":[4.814136e-7,0.0001769003,0.00005557085,0.0001440306,0.00004055591,0.0001010676,0.00000795614,0.0002871029,0.000009295269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002684799,"about_ca_system_score_gemma":0.000007451528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004434517,"about_ca_topic_score_gemma":0.00002617282,"domain_scores_codex":[0.9992282,0.000008567871,0.00019853,0.0002333114,0.0001033328,0.0002279982],"domain_scores_gemma":[0.9995862,0.00006729561,0.00001783425,0.0001920593,0.00003462083,0.0001019886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007414083,0.00002642852,0.00004497982,0.00003719452,0.00001288926,4.680739e-7,0.00007288697,0.009956938,0.1336735,0.00003516408,0.00007654748,0.8560556],"study_design_scores_gemma":[0.001126627,0.00002392427,0.0007558299,0.00002300902,0.00001865565,0.000012103,0.000006786653,0.5976339,0.1279861,0.0002137944,0.2718894,0.0003098774],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001128012,0.00002984167,0.9967467,0.0001470887,0.00005777525,0.0006796792,0.00004685306,0.0003478689,0.0008162185],"genre_scores_gemma":[0.1266903,0.00001525537,0.8726122,0.000119053,0.0001988798,0.0002424914,0.00004077606,0.00004241034,0.00003857515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8557457,"threshold_uncertainty_score":0.7213783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102670916422392,"score_gpt":0.2317636704722032,"score_spread":0.2107369613079793,"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."}}