{"id":"W2541857441","doi":"10.1109/icspc.2007.4728584","title":"An Imagewatermarking Scheme based on Wavelet and Multiple-Parameter Fractional Fourier Transforms","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Discrete wavelet transform; Harmonic wavelet transform; Wavelet transform; Fractional Fourier transform; Robustness (evolution); Wavelet; Discrete Fourier transform (general); Algorithm; Second-generation wavelet transform; Digital watermarking; Mathematics; Computer science; Fourier transform; Stationary wavelet transform; Artificial intelligence; Computer vision; Image (mathematics); Fourier analysis; Mathematical analysis","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":[0.0005264443,0.0001841558,0.0001322787,0.0002553929,0.00019817,0.0001511915,0.000359784,0.0000906829,0.00001337277],"category_scores_gemma":[0.00001688515,0.0001404964,0.00007079091,0.0001990843,0.00007662785,0.001038021,0.0000328366,0.0002041292,0.000002541299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002039278,"about_ca_system_score_gemma":0.00001254758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001085996,"about_ca_topic_score_gemma":0.00000627335,"domain_scores_codex":[0.9986907,0.00002980481,0.0002174524,0.0004225928,0.0002761384,0.0003633285],"domain_scores_gemma":[0.9990876,0.0002420658,0.00004816702,0.0004359665,0.00004568951,0.00014052],"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":[0.0006877883,0.0008746986,0.06320256,0.00007688609,0.00007149917,0.0002822719,0.001132943,0.0003004499,0.06232221,0.03826001,0.0003609802,0.8324277],"study_design_scores_gemma":[0.0009965745,0.0004302978,0.02109685,0.0000461887,0.00000522746,0.00003833232,0.00001705459,0.7269121,0.2291473,0.0131401,0.007580698,0.0005893506],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07079734,0.000005272305,0.9260924,0.0002464293,0.00008844827,0.0001454039,0.000002058982,0.0004535643,0.002169119],"genre_scores_gemma":[0.5614447,0.000001925907,0.4376968,0.0007936936,0.00002860616,0.000003886935,0.00000400972,0.000007191482,0.00001919123],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8318384,"threshold_uncertainty_score":0.5729275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01268772259915566,"score_gpt":0.2646772355876828,"score_spread":0.2519895129885271,"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."}}