{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001576704,0.00008675626,0.000081274,0.00006928782,0.0001178209,0.0001117453,0.000251843,0.00005506514,0.000002463219],"category_scores_gemma":[0.000006557525,0.00007061948,0.0000783435,0.0001078026,0.00002825709,0.0005676979,0.00002882638,0.00005532026,0.000002901812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002184581,"about_ca_system_score_gemma":0.000006801585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000203762,"about_ca_topic_score_gemma":0.000009989209,"domain_scores_codex":[0.9993808,0.00002001783,0.0001223089,0.0002175091,0.00009558179,0.0001637628],"domain_scores_gemma":[0.9994978,0.00004722052,0.0000721996,0.0002678538,0.00009114319,0.00002375461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000186177,0.00007964019,0.0001672168,0.00001309572,0.000007449141,0.000003724775,0.0001033052,0.000001717882,0.1800214,0.7950192,0.01581036,0.008754237],"study_design_scores_gemma":[0.0008516866,0.0001467783,0.008157005,0.00002905138,0.00001112049,0.00003839988,0.00005963232,0.00571197,0.291118,0.5867151,0.106773,0.0003882397],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00403507,0.0000367583,0.9896744,0.002195811,0.0001366738,0.0003214132,0.000003376783,0.0005111572,0.00308536],"genre_scores_gemma":[0.3730402,0.000002636142,0.6253353,0.0001269906,0.0000607402,0.00006818923,0.00001265589,0.000004625857,0.001348629],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3690052,"threshold_uncertainty_score":0.2879779,"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."}}