{"id":"W1978107848","doi":"10.1016/j.eswa.2014.01.023","title":"Robust logo watermarking using biometrics inspired key generation","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Winnipeg; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Biometrics; Computer science; Key (lock); Digital watermarking; Logo (programming language); Artificial intelligence; Computer vision; Watermark; Image (mathematics); Computer security; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002090078,0.0003784354,0.0003937122,0.000490271,0.0002442447,0.0005850167,0.0003220918,0.0007908616,0.001694769],"category_scores_gemma":[0.001092362,0.0001622807,0.0002434347,0.0003497767,0.0003605825,0.001160481,0.0006694117,0.000478476,0.0007889712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001872612,"about_ca_system_score_gemma":0.0001736743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006528503,"about_ca_topic_score_gemma":0.0001296291,"domain_scores_codex":[0.9997503,0.00004243014,0.00001212097,0.00005673807,0.0001092544,0.00002912842],"domain_scores_gemma":[0.9995963,0.000115243,0.0001046095,0.0001104741,0.00005978004,0.00001352849],"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":[0.000459299,0.00008863497,0.0005752117,0.0001603987,0.00003161475,0.0003182072,0.00007106721,0.008928522,0.8179327,0.01629319,0.0008243709,0.1543168],"study_design_scores_gemma":[0.00005439821,0.0003653925,0.001930476,0.00004394535,0.00006091033,0.001924136,0.00006054235,0.3370303,0.6414986,0.008945707,0.008020872,0.00006486963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1688781,0.001201545,0.8209813,0.0004870775,0.0002509659,0.00007550952,0.0001225336,0.001341545,0.006661363],"genre_scores_gemma":[0.8626554,0.000515619,0.1293044,0.0001290255,0.00008038423,0.0000378679,0.00009527674,0.00007832229,0.007103704],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001694769,"threshold_uncertainty_score":0.005669594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05099420474541649,"score_gpt":0.2639735201946812,"score_spread":0.2129793154492647,"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."}}