{"id":"W3082349461","doi":"10.1007/s11042-020-09606-x","title":"Image watermarking using soft computing techniques: A comprehensive survey","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brandon University","funders":"","keywords":"Digital watermarking; Computer science; Watermark; Robustness (evolution); Embedding; Scheme (mathematics); Digital Watermarking Alliance; Soft computing; Image (mathematics); Data mining; Computer security; Theoretical computer science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001528884,0.0001867971,0.0002217674,0.00006661283,0.0003510293,0.0002638963,0.000525117,0.00007100371,0.000001723061],"category_scores_gemma":[0.00002662401,0.0001763157,0.00005586635,0.0004355931,0.0001255785,0.0004864363,0.0004123896,0.0001893147,0.000005250381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001423915,"about_ca_system_score_gemma":0.00002042145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003194203,"about_ca_topic_score_gemma":0.000001365401,"domain_scores_codex":[0.9987534,0.00007918985,0.0002783365,0.0004792682,0.0001345042,0.000275331],"domain_scores_gemma":[0.9989942,0.0002647151,0.0001243985,0.0003313908,0.0001288958,0.0001564435],"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":[0.00001763485,0.00009669979,0.008150183,0.0001373671,0.00004610676,0.00001498307,0.002178071,0.00005541418,0.1281476,0.001889199,0.0003708693,0.8588958],"study_design_scores_gemma":[0.0005402345,0.00009680607,0.01890937,0.0001090139,0.00002535934,0.00004285699,0.00009843303,0.8837548,0.04811451,0.002653499,0.0446388,0.001016331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006511215,0.00009685689,0.9915111,0.0003448292,0.00002241703,0.0005676027,0.00004301984,0.0007502406,0.0001526899],"genre_scores_gemma":[0.3443463,0.0000373887,0.6547755,0.0006152932,0.0001079823,0.00006244088,0.00003907676,0.00001435787,0.000001732168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8836994,"threshold_uncertainty_score":0.7189945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05991109252344225,"score_gpt":0.2999709975504639,"score_spread":0.2400599050270216,"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."}}