{"id":"W2554679165","doi":"10.1007/s11042-016-4150-3","title":"Framework for robust blind image watermarking based on classification of attacks","year":2016,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Digital watermarking; Computer science; Watermark; Robustness (evolution); Artificial intelligence; Computer security; Image (mathematics); Computer vision; The Internet; Digital Watermarking Alliance; Data mining; Pattern recognition (psychology); World Wide Web","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":[0.001631008,0.001147082,0.001673124,0.001808981,0.0005921001,0.002033776,0.002208588,0.001898614,0.002459305],"category_scores_gemma":[0.001938343,0.0004174602,0.001596256,0.001105017,0.00109218,0.001635172,0.002392682,0.001698561,0.001947939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008492578,"about_ca_system_score_gemma":0.001716445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002373421,"about_ca_topic_score_gemma":0.002472959,"domain_scores_codex":[0.9987103,0.0002726308,0.00008567402,0.0002486784,0.0005345641,0.0001481388],"domain_scores_gemma":[0.9991622,0.0001610358,0.0001186661,0.0001963175,0.0002987792,0.00006308418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003144998,0.0002794121,0.0008770482,0.0003238274,0.0002336402,0.0003286419,0.0001755962,0.240334,0.05916628,0.365016,0.006557315,0.3263937],"study_design_scores_gemma":[0.00001640556,0.00007657886,0.0001596817,0.00001952272,0.00003497453,0.0001352426,0.00001689836,0.948146,0.005533572,0.04079,0.005041497,0.0000296112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007047157,0.00009539467,0.998472,0.00003642468,0.00002080354,0.00002095275,0.00002449464,0.0002172104,0.0004079904],"genre_scores_gemma":[0.1001231,0.0007554434,0.8924526,0.000121739,0.0001851488,0.0002148603,0.0003816306,0.0001558513,0.005609558],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002459305,"threshold_uncertainty_score":0.008625686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05766387832023236,"score_gpt":0.3122683929181744,"score_spread":0.2546045145979421,"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."}}