{"id":"W2140470795","doi":"10.1109/icassp.2007.365987","title":"Block Size Forensic Analysis in Digital Images","year":2007,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Block (permutation group theory); Computer science; Block size; Artificial intelligence; Context (archaeology); Pattern recognition (psychology); False alarm; Digital image; Computer vision; Image processing; Digital forensics; Image (mathematics); Mathematics; Key (lock); Computer security","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":[0.0002323634,0.0001035686,0.0001579093,0.0003713784,0.00001954286,0.0002927719,0.0003395981,0.00003875461,0.00001001203],"category_scores_gemma":[0.0002646366,0.00009033724,0.0001061815,0.002238438,0.0000481652,0.001206633,0.0001480259,0.00007399303,0.00009861851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005600174,"about_ca_system_score_gemma":0.00001885049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003824381,"about_ca_topic_score_gemma":0.0004099284,"domain_scores_codex":[0.9988806,0.000004125147,0.0002368315,0.000304218,0.0002665971,0.0003075963],"domain_scores_gemma":[0.9991441,0.0002772685,0.00004375175,0.000381741,0.00005766144,0.00009545573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001713665,0.0001729539,0.04603073,0.000006407514,0.0001750829,0.0002250596,0.0003436965,0.0002609148,0.0004486217,0.0143677,0.001311409,0.9366403],"study_design_scores_gemma":[0.001897516,0.0005369131,0.7740798,0.00003264233,0.0001305269,0.0001513472,0.0004089134,0.03731961,0.1163303,0.06291938,0.004628098,0.001565027],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5119559,0.00001431898,0.3526986,0.0002128697,0.000381854,0.0001036529,0.000001630956,0.0002970421,0.1343341],"genre_scores_gemma":[0.986596,5.342224e-7,0.01158097,0.0001219529,0.00003178918,0.000001833287,0.000001073176,0.000004692818,0.001661164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9350753,"threshold_uncertainty_score":0.3683846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005319055626112228,"score_gpt":0.217362042221617,"score_spread":0.2120429865955047,"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."}}