{"id":"W2548185388","doi":"10.1109/iecon.2006.347302","title":"Data Hiding Scheme With Geometric Distortions Correction","year":2006,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of the IEEE Industrial Electronics Society","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Robustness (evolution); Artificial intelligence; Computer vision; Feature (linguistics); Computer science; Digital watermarking; Information hiding; Channel (broadcasting); Rotation (mathematics); Feature extraction; Mathematics; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001628345,0.0004613459,0.0005836575,0.0005943272,0.0004144816,0.0003044437,0.0007554949,0.0004295721,0.001091635],"category_scores_gemma":[0.00050086,0.0002332924,0.0004426609,0.0005492888,0.0003801162,0.001446278,0.000861455,0.0005250566,0.0004565929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003317176,"about_ca_system_score_gemma":0.0002787364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003536639,"about_ca_topic_score_gemma":0.0004070982,"domain_scores_codex":[0.999686,0.00002243071,0.00002179012,0.00006912719,0.0001665788,0.00003406395],"domain_scores_gemma":[0.9996855,0.00004446191,0.0000570196,0.00010735,0.00008668461,0.00001907161],"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":[0.0003234567,0.0000753042,0.000827971,0.0003686788,0.00008027328,0.0002868888,0.0002728929,0.01454539,0.5769096,0.02031977,0.001501681,0.3844881],"study_design_scores_gemma":[0.0001371091,0.0007545562,0.001919418,0.00003963365,0.0001485448,0.002678695,0.00007356151,0.274954,0.6787717,0.00631496,0.03406332,0.0001445444],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09190603,0.001300849,0.9016873,0.0002563712,0.0002369583,0.00012598,0.00008754309,0.001031687,0.003367398],"genre_scores_gemma":[0.6306748,0.001041878,0.3570322,0.0001376383,0.000149877,0.0001141532,0.0002277026,0.00007847908,0.01054326],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001091635,"threshold_uncertainty_score":0.003651857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04244876966402345,"score_gpt":0.2475987737074651,"score_spread":0.2051500040434417,"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."}}