{"id":"W2770308584","doi":"10.1016/j.ins.2017.11.027","title":"JPEG based steganography methods using Cohort Intelligence with Cognitive Computing and modified Multi Random Start Local Search optimization algorithms","year":2017,"lang":"en","type":"article","venue":"Information Sciences","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Steganography; JPEG; Computer science; Algorithm; Optimization problem; Distortion (music); Steganography tools; Artificial intelligence; Image quality; Data compression; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008789175,0.000638417,0.0008922567,0.0007940665,0.000392705,0.0007383047,0.001035855,0.0007570202,0.001266441],"category_scores_gemma":[0.002330636,0.000293426,0.0006962101,0.0007425786,0.0005942145,0.001218387,0.0009552253,0.0007855772,0.0002520615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005872893,"about_ca_system_score_gemma":0.0007938115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002738282,"about_ca_topic_score_gemma":0.003337989,"domain_scores_codex":[0.9996717,0.00009917539,0.00001969426,0.00006125982,0.0001164787,0.00003180604],"domain_scores_gemma":[0.9992614,0.0004074933,0.00007914364,0.00005489041,0.0001681328,0.00002904399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001492325,0.0001574883,0.0009653544,0.0001381372,0.0001360053,0.00007469209,0.0001056486,0.7561162,0.008747939,0.04657105,0.001103771,0.1857344],"study_design_scores_gemma":[0.000005560417,0.00001875396,0.00006031935,0.000002362186,0.00000651162,0.000009260307,0.000003323828,0.9976196,0.0005962685,0.00154853,0.0001255881,0.000003930709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01007953,0.000133015,0.9887177,0.00004616778,0.00002274377,0.00002371329,0.000009842304,0.00009363123,0.0008736138],"genre_scores_gemma":[0.3689784,0.0003045692,0.6260412,0.000106749,0.00006666118,0.0001771049,0.00008133422,0.00009694834,0.004147017],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002738282,"threshold_uncertainty_score":0.005444705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07461330641246706,"score_gpt":0.3828527341544694,"score_spread":0.3082394277420023,"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."}}