{"id":"W2155843867","doi":"10.1109/ccece.2009.5090126","title":"A new image watermarking algorithm based on wavelet transform","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Digital watermarking; Watermark; Histogram equalization; Artificial intelligence; Computer vision; Robustness (evolution); Embedding; Grayscale; Computer science; Wavelet; Wavelet transform; Mathematics; Binary image; Human visual system model; Histogram; Algorithm; Image processing; Pixel; 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":[0.0003267689,0.0005519228,0.0007479072,0.001155132,0.00033666,0.000588822,0.0007375421,0.0008342063,0.001332564],"category_scores_gemma":[0.0006714733,0.0003053201,0.0006010174,0.001093151,0.0003883357,0.002050949,0.0005900628,0.0008858151,0.0008769313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002257921,"about_ca_system_score_gemma":0.0003350724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003797154,"about_ca_topic_score_gemma":0.0004065018,"domain_scores_codex":[0.9996025,0.00003392307,0.00003009028,0.00008064989,0.0002265576,0.00002633537],"domain_scores_gemma":[0.9997925,0.00004092182,0.00003121587,0.00003041753,0.00009126761,0.00001364293],"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":[0.0002021787,0.00006539134,0.0005188363,0.0004576129,0.00009340393,0.0002498127,0.0000898885,0.01323968,0.2522441,0.01845974,0.003318113,0.7110611],"study_design_scores_gemma":[0.000185078,0.000560194,0.002005015,0.0001000885,0.000207087,0.003156348,0.00006647722,0.6131018,0.2706414,0.01098991,0.09883512,0.0001515876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007836555,0.001610845,0.9881125,0.0001327955,0.0002961922,0.00006257746,0.00004272844,0.0005320078,0.001373743],"genre_scores_gemma":[0.09498196,0.003408101,0.8921841,0.0001657723,0.0002919456,0.0001390714,0.0003030586,0.0001317517,0.008394271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001332564,"threshold_uncertainty_score":0.004457831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007862664653017102,"score_gpt":0.2385465236853782,"score_spread":0.2306838590323611,"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."}}