{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006477414,0.0008648401,0.000860785,0.001947746,0.0003203801,0.0006283548,0.001084469,0.001160358,0.002074448],"category_scores_gemma":[0.001557194,0.0004307851,0.000785779,0.0009752673,0.000645108,0.001259385,0.0006992415,0.0009699367,0.001991383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003510462,"about_ca_system_score_gemma":0.000428074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005538281,"about_ca_topic_score_gemma":0.0006254709,"domain_scores_codex":[0.9989361,0.0000880217,0.00005997077,0.0002401534,0.000631883,0.00004385489],"domain_scores_gemma":[0.9993331,0.0001421898,0.0001315811,0.0001704669,0.0001983047,0.00002429377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001722892,0.00006859927,0.0007152491,0.0005334567,0.0001418473,0.0002821617,0.00009137611,0.006369813,0.3970257,0.006889535,0.004174666,0.5835353],"study_design_scores_gemma":[0.0000785587,0.000598651,0.00465955,0.0001357314,0.0001902106,0.0049862,0.00008470898,0.3316429,0.5905689,0.006153275,0.06069003,0.0002112719],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007250984,0.00194156,0.9871944,0.0001270768,0.0001775548,0.00008281957,0.0001300711,0.001662821,0.001432743],"genre_scores_gemma":[0.1353551,0.002436276,0.8530203,0.0002669474,0.0002260892,0.0001812493,0.0004354833,0.0001581727,0.007920442],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002074448,"threshold_uncertainty_score":0.006939709,"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."}}