{"id":"W116021867","doi":"","title":"Geometrically robust image watermarking using star patterns","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Signal and Image Processing","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Digital watermarking; Computer science; BitTorrent tracker; The Internet; Noise (video); Image (mathematics); Computer vision; Point (geometry); Copy protection; Digital image; Artificial intelligence; Digital signature; Digital Watermarking Alliance; Image processing; Computer security; Mathematics; World Wide Web; Eye tracking","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":[0.0005138547,0.0002457761,0.0001977164,0.0005636952,0.0002736135,0.0008948536,0.000773717,0.00007568442,0.00003620269],"category_scores_gemma":[0.00002142835,0.0002128222,0.00006787846,0.0003371567,0.0001215618,0.001852727,0.000184411,0.000297515,0.000004497944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006279745,"about_ca_system_score_gemma":0.00005242419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002567982,"about_ca_topic_score_gemma":0.000002717605,"domain_scores_codex":[0.9981465,0.00003467131,0.0003834006,0.0005231518,0.0005003249,0.0004119526],"domain_scores_gemma":[0.9989851,0.00006771447,0.000205642,0.0001757571,0.0004377282,0.0001280323],"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":[0.0001954374,0.0002450012,0.003834542,0.0001461403,0.00007021763,0.0004856391,0.001117398,0.0000620166,0.5353987,0.008111529,0.00005666151,0.4502768],"study_design_scores_gemma":[0.0009825282,0.0003364128,0.007725663,0.001225521,0.00002583873,0.0002717824,0.0002371537,0.4987324,0.4598544,0.02872664,0.000584005,0.001297697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1103882,0.00003573551,0.8856896,0.0002403076,0.000202902,0.00009006908,0.000006532983,0.0001997097,0.003146915],"genre_scores_gemma":[0.8011625,0.00003269649,0.1983336,0.0002793523,0.0001264132,0.000004002008,0.00000433562,0.00001253676,0.00004453124],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6907743,"threshold_uncertainty_score":0.8678637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05891456688397469,"score_gpt":0.31642133850487,"score_spread":0.2575067716208953,"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."}}