{"id":"W2151686053","doi":"10.1109/tcsvt.2003.815959","title":"RST-invariant digital image watermarking based on log-polar mapping and phase correlation","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":172,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Watermark; Invariant (physics); Mathematics; Digital watermarking; Phase correlation; Scaling; Translation (biology); Artificial intelligence; Computer vision; Algorithm; Fourier transform; Image (mathematics); Computer science; Fourier analysis; Mathematical analysis; Geometry; Fractional Fourier transform","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":[],"consensus_categories":[],"category_scores_codex":[0.0003141663,0.0002393507,0.0002895463,0.0007802804,0.0004866229,0.0002584246,0.0002290632,0.0002188978,8.700966e-7],"category_scores_gemma":[0.00001958126,0.000216834,0.00007840226,0.0004102137,0.0001373816,0.000547352,0.000003116084,0.0002609181,0.000001721908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004326832,"about_ca_system_score_gemma":0.00002544509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005973804,"about_ca_topic_score_gemma":0.000001415964,"domain_scores_codex":[0.9985496,0.00005863945,0.0003497389,0.000547673,0.0001410987,0.0003532662],"domain_scores_gemma":[0.999095,0.0001819063,0.0001324138,0.000439446,0.0000751027,0.00007611805],"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":[0.0001827264,0.001584508,0.001196947,0.001028241,0.0003407671,0.0002244895,0.00144451,0.00487162,0.06600935,0.1984992,0.0002500338,0.7243677],"study_design_scores_gemma":[0.009114179,0.004265083,0.00009354163,0.001491825,0.0001155595,0.001123867,0.0005241886,0.7661294,0.1330554,0.04257798,0.03935153,0.002157402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01142749,0.0001404365,0.9861762,0.0001929521,0.000470538,0.0006520912,0.00005227466,0.0006394935,0.0002485058],"genre_scores_gemma":[0.9950846,0.00003042543,0.004562168,0.00007978964,0.00001243784,0.00017536,0.00000332979,0.00002045408,0.00003140752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9836571,"threshold_uncertainty_score":0.8842236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01798566070721255,"score_gpt":0.2434006264136927,"score_spread":0.2254149657064801,"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."}}