{"id":"W2106993989","doi":"10.1109/mmsp.2001.962787","title":"A robust multimedia watermarking technique using Zernike transform","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Zernike polynomials; Digital watermarking; Watermark; Computer vision; Artificial intelligence; Computer science; Rotation (mathematics); Invariant (physics); Discrete cosine transform; JPEG; Transform coding; Robustness (evolution); Data compression; Image (mathematics); Mathematics; Optics","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":[0.0002317594,0.0002266791,0.0001947753,0.0002611315,0.0002259358,0.0001339157,0.0008885023,0.0001229022,0.00005855791],"category_scores_gemma":[0.00000665478,0.0001860936,0.0001370108,0.0004637662,0.00008769469,0.0009977123,0.0001452992,0.0002140953,0.00001296117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004306686,"about_ca_system_score_gemma":0.000006842004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002676639,"about_ca_topic_score_gemma":0.000003714017,"domain_scores_codex":[0.9985318,0.00004730137,0.000286967,0.0004286252,0.000235025,0.0004702843],"domain_scores_gemma":[0.9991938,0.0000369192,0.00006465802,0.0005501985,0.00004893114,0.0001054736],"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":[0.0000217249,0.0005837801,0.001642076,0.000160717,0.0001105661,0.0005745445,0.005747066,0.0010237,0.2552569,0.02250542,0.002213681,0.7101598],"study_design_scores_gemma":[0.0003750267,0.00009749907,0.00008650761,0.0001231845,0.00001213514,0.0003241173,0.00001737251,0.5179361,0.4552842,0.01363939,0.01134143,0.0007630109],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001859632,0.00007062683,0.9854916,0.0003025306,0.0001277141,0.0003755545,0.000001333253,0.001504003,0.01026695],"genre_scores_gemma":[0.4528273,0.00002566175,0.5467885,0.0001097741,0.0000314944,0.00003630474,7.851057e-7,0.00001236202,0.0001677863],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7093968,"threshold_uncertainty_score":0.7588677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04626710750807533,"score_gpt":0.2440804530034004,"score_spread":0.1978133454953251,"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."}}