{"id":"W2002447637","doi":"10.1145/2043674.2043698","title":"Hiding depth map into stereo image in JPEG format using reversible watermarking","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Communications Research Centre Canada; University of Ottawa","funders":"","keywords":"Digital watermarking; Computer vision; Artificial intelligence; Computer science; Stereo image; JPEG; JPEG 2000; Depth map; Image (mathematics); Information hiding; Computer graphics (images); Image processing; Image compression","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":[0.0001102691,0.0003069625,0.000208953,0.0005133367,0.0001428068,0.000253539,0.0003342311,0.0002771391,0.001470377],"category_scores_gemma":[0.0002916805,0.0001678437,0.0003241462,0.0003753046,0.0001946165,0.0006039062,0.0002632785,0.0002840396,0.0004433996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001591199,"about_ca_system_score_gemma":0.0002129158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005197533,"about_ca_topic_score_gemma":0.001129333,"domain_scores_codex":[0.9998887,0.000006033383,0.000004587085,0.00001350883,0.00007746474,0.000009787154],"domain_scores_gemma":[0.9998792,0.00002593394,0.00002842335,0.00003161937,0.00002843663,0.000006316772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001105334,0.00005193141,0.0003942931,0.0002266634,0.00002374853,0.000348642,0.00007505,0.003686085,0.7860975,0.00470046,0.000558447,0.2037266],"study_design_scores_gemma":[0.00004403698,0.0004456197,0.00209813,0.0000352307,0.00007948284,0.002333757,0.0000578629,0.06063597,0.9127245,0.001757483,0.01972459,0.00006318911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2188966,0.002485617,0.7633269,0.0003550959,0.0001986137,0.0001918999,0.0002888586,0.001361394,0.01289507],"genre_scores_gemma":[0.5765243,0.003096043,0.4085754,0.0001258743,0.0001592288,0.00005673327,0.0003592437,0.00009883123,0.01100439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001470377,"threshold_uncertainty_score":0.004918933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0420835214981784,"score_gpt":0.2666788399980955,"score_spread":0.2245953184999171,"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."}}