{"id":"W2111081336","doi":"10.1007/s11042-015-2475-y","title":"Hiding depth information in compressed 2D image/video using reversible watermarking","year":2015,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Digital watermarking; Watermark; Information hiding; Image quality; Bitstream; Embedding; Decoding methods; Algorithm; Image (mathematics)","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":[0.0002363509,0.0001115738,0.0001271066,0.0001829232,0.0001407429,0.0002500733,0.0003294082,0.00005555426,8.105943e-7],"category_scores_gemma":[0.00002404554,0.0001063496,0.00002650116,0.0003694739,0.0000521926,0.002392734,0.0001918921,0.0001162463,0.000006378049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004211167,"about_ca_system_score_gemma":0.00002871084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005664519,"about_ca_topic_score_gemma":0.000004628246,"domain_scores_codex":[0.9991869,0.00003151511,0.0002486357,0.0001919152,0.0001324848,0.0002084951],"domain_scores_gemma":[0.9993632,0.00007213754,0.00009697249,0.0002911444,0.00007822741,0.00009828923],"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":[0.00003574163,0.0001800422,0.02103926,0.000151697,0.00002844736,0.00001127523,0.008498051,0.002080094,0.02584986,0.01349111,0.001257567,0.9273769],"study_design_scores_gemma":[0.001587297,0.00004303428,0.007254842,0.0001651747,0.00001661617,0.00003437011,0.0003460441,0.8602298,0.03254312,0.02086173,0.07623886,0.0006791384],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01332558,0.00007115372,0.9845323,0.0001734214,0.00004893688,0.0004337648,0.000008450265,0.0002164444,0.001189978],"genre_scores_gemma":[0.5633473,0.00003190586,0.4363236,0.0001001574,0.00003510241,0.0001237694,0.00002769624,0.000004855057,0.000005690689],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9266977,"threshold_uncertainty_score":0.4336812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04698705642131965,"score_gpt":0.2846234314718675,"score_spread":0.2376363750505478,"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."}}