{"id":"W2084930660","doi":"10.4236/jis.2012.32011","title":"A Robust Method to Detect Hidden Data from Digital Images","year":2012,"lang":"en","type":"article","venue":"Journal of Information Security","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Digital image; Pattern recognition (psychology); Data mining; Image (mathematics); Image processing","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009491048,0.00009509428,0.0001580102,0.0002332365,0.00006636378,0.0003740934,0.00136239,0.00004600189,0.000006727362],"category_scores_gemma":[0.000161923,0.00007478827,0.00006632463,0.0002742901,0.00001271509,0.02081721,0.0005317947,0.0001968195,0.00002335475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002938807,"about_ca_system_score_gemma":0.00003261305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006833951,"about_ca_topic_score_gemma":4.449349e-7,"domain_scores_codex":[0.9988947,0.00005109529,0.0004818752,0.00006662468,0.0003220354,0.0001836489],"domain_scores_gemma":[0.9986386,0.0001186161,0.0004162168,0.0004842302,0.0001827636,0.0001596306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001284023,0.0001112534,0.003513,0.00004252564,0.0001350822,0.00001085117,0.0156667,0.0001267651,0.0004277527,0.005224057,0.04871138,0.9259022],"study_design_scores_gemma":[0.001929514,0.0007114728,0.03331859,0.0004157201,0.00008705894,0.0009736454,0.0009384213,0.01952581,0.08635601,0.1669315,0.687318,0.001494259],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004768106,0.00008923905,0.9933779,0.0003210735,0.000224441,0.00006773627,0.00006683209,0.00006865129,0.001015998],"genre_scores_gemma":[0.392307,0.00001015601,0.6072862,0.0002688771,0.000112763,8.51245e-7,0.00001060638,0.000001999882,0.000001543783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.924408,"threshold_uncertainty_score":0.9928781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03624251644040574,"score_gpt":0.3002015310192102,"score_spread":0.2639590145788044,"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."}}