{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000428285,0.0005505418,0.0006296508,0.00110601,0.0002843571,0.0008201317,0.0006253592,0.0008928286,0.001666216],"category_scores_gemma":[0.002189558,0.0002667107,0.0005115623,0.001227272,0.0006666305,0.001795002,0.0009019687,0.0004573918,0.001033448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002067081,"about_ca_system_score_gemma":0.0002294021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002228139,"about_ca_topic_score_gemma":0.000238373,"domain_scores_codex":[0.9994424,0.0001122148,0.00003314947,0.0000927434,0.0002864754,0.0000331412],"domain_scores_gemma":[0.998956,0.0002930815,0.0002031143,0.0003247748,0.0001923394,0.00003070664],"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":[0.0003235647,0.00009369539,0.0008477409,0.0001977449,0.00005236853,0.0003288756,0.0001283973,0.0513911,0.4019835,0.02372983,0.001187266,0.5197359],"study_design_scores_gemma":[0.00006721487,0.0005398629,0.001420754,0.00003848952,0.00004966802,0.002245304,0.00007026867,0.6210672,0.3457843,0.01559315,0.01303934,0.00008443023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05872057,0.0004938685,0.9357449,0.0001213758,0.00008430899,0.00006384232,0.00003155503,0.0007521191,0.003987395],"genre_scores_gemma":[0.4115347,0.0007892224,0.5812955,0.00007802575,0.00009011517,0.00006089867,0.0001204998,0.000159164,0.005871851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001666216,"threshold_uncertainty_score":0.005574048,"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."}}