{"id":"W2754259890","doi":"10.1007/s11042-018-6129-8","title":"Hierarchical watermarking framework based on analysis of local complexity variations","year":2018,"lang":"en","type":"preprint","venue":"Multimedia Tools and Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University; University of British Columbia","funders":"","keywords":"Digital watermarking; Watermark; Discrete cosine transform; Robustness (evolution); Embedding; Computer science; Block (permutation group theory); Hierarchy; Artificial intelligence; Transparency (behavior); Image (mathematics); Theoretical computer science; Mathematics; Data mining; Algorithm; Pattern recognition (psychology); Computer security","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":[0.0002941655,0.0005370229,0.0004784185,0.001070214,0.0002456473,0.0006790303,0.0005761763,0.0004651829,0.001894374],"category_scores_gemma":[0.0007748703,0.0001823877,0.0004889866,0.0008443281,0.0003625076,0.001009043,0.0006269818,0.0005874797,0.0004186394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004000024,"about_ca_system_score_gemma":0.0004518355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001148533,"about_ca_topic_score_gemma":0.00141487,"domain_scores_codex":[0.9997301,0.00004221403,0.00001182973,0.00005629839,0.0001306817,0.00002893133],"domain_scores_gemma":[0.999688,0.00007957264,0.00005773794,0.00005952241,0.00009104318,0.00002416708],"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":[0.0002556712,0.0001202912,0.001275298,0.0002483307,0.0001129713,0.0002764043,0.0001693271,0.2498188,0.2845228,0.1472762,0.002382973,0.3135408],"study_design_scores_gemma":[0.000005749963,0.00004977002,0.00072544,0.000005629131,0.00002741255,0.00009012616,0.00001112742,0.9752597,0.01297653,0.009276997,0.001553379,0.00001811913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01206709,0.0002435075,0.9860746,0.00003689607,0.00002226488,0.00002453588,0.00005303142,0.0001846997,0.001293361],"genre_scores_gemma":[0.49481,0.001039934,0.4974732,0.00006818189,0.0001564214,0.00009251748,0.0003144061,0.000176177,0.00586922],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001894374,"threshold_uncertainty_score":0.006337345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04020560069934389,"score_gpt":0.3043163546980119,"score_spread":0.2641107539986681,"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."}}