{"id":"W2169755605","doi":"10.1109/mmsp.2006.285297","title":"Contourlet Domain Feature Extraction for Image Content Authentication","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Contourlet; Computer science; Digital watermarking; Lossy compression; Artificial intelligence; Feature extraction; JPEG; Watermark; Pattern recognition (psychology); Authentication (law); Wavelet; Computer vision; Redundancy (engineering); Feature (linguistics); Wavelet transform; Image (mathematics); 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":[0.000422012,0.0003106623,0.0003368264,0.0008873867,0.0001517819,0.0004340281,0.0003231961,0.0004925581,0.001703789],"category_scores_gemma":[0.001148476,0.0001697741,0.0002690571,0.0008149481,0.0002780302,0.000673975,0.0002696007,0.0003658367,0.00120607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190942,"about_ca_system_score_gemma":0.0002490029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001699061,"about_ca_topic_score_gemma":0.000179356,"domain_scores_codex":[0.9998239,0.00003195372,0.000009588686,0.00002428712,0.00009731279,0.00001315482],"domain_scores_gemma":[0.9996457,0.0001163889,0.00004157719,0.00009429848,0.0000872477,0.00001479193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002464146,0.00006748047,0.0005837417,0.0001292534,0.00001871052,0.0001577479,0.00005049544,0.009973614,0.5001189,0.006134743,0.001362353,0.4811566],"study_design_scores_gemma":[0.0000471735,0.0004124677,0.003965292,0.00003585843,0.00007822116,0.0009608553,0.00003825798,0.5032473,0.4691219,0.006928134,0.01512012,0.0000443882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05622062,0.0007005666,0.9403169,0.0001595346,0.00005055628,0.00005457265,0.0001006238,0.0006309259,0.001765782],"genre_scores_gemma":[0.3694117,0.001153366,0.6248004,0.000094726,0.00008933683,0.00006897493,0.0003731803,0.0001105955,0.00389765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001703789,"threshold_uncertainty_score":0.005699754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673923947627633,"score_gpt":0.2665808754594623,"score_spread":0.249841635983186,"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."}}