{"id":"W3119660979","doi":"10.18280/ts.370601","title":"Boosting up Source Scanner Identification Using Wavelets and Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche","keywords":"Scanner; Computer science; Artificial intelligence; Convolutional neural network; Boosting (machine learning); Pattern recognition (psychology); Block (permutation group theory); Wavelet; Diagonal; Identification (biology); Computer vision; Artificial neural network; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018022,0.0001253212,0.0001101089,0.00004911649,0.0001499915,0.000288919,0.000211282,0.0000375307,0.00001607253],"category_scores_gemma":[0.00003961426,0.0001291909,0.00003847678,0.0002371415,0.00007153311,0.000729198,0.000130844,0.0001080976,0.00000724249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000425377,"about_ca_system_score_gemma":0.00002281319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009278269,"about_ca_topic_score_gemma":0.000001340161,"domain_scores_codex":[0.9987773,0.00003986808,0.0002732283,0.000356376,0.0003162195,0.0002370064],"domain_scores_gemma":[0.9994959,0.00005696091,0.0001205163,0.0001039394,0.00006891323,0.0001537744],"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":[0.00009030264,0.00009263569,0.003467488,0.00007627576,0.00009713722,0.00003277131,0.003676019,0.1283366,0.03797432,0.007533495,0.00222366,0.8163993],"study_design_scores_gemma":[0.0003276496,0.00006460487,0.003457474,0.00001264047,0.000009550423,0.00003145076,0.00003478391,0.9943566,0.0009751237,0.0001449317,0.0004469724,0.000138227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3137883,0.00004568413,0.6848373,0.0006260297,0.0003804699,0.0001304395,0.000001607837,0.0001294173,0.00006069436],"genre_scores_gemma":[0.9966674,8.695415e-7,0.002275948,0.000650376,0.0003460812,0.000006675292,0.000008044217,0.000009950777,0.00003459177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.86602,"threshold_uncertainty_score":0.526825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02762841135686241,"score_gpt":0.2185169160580843,"score_spread":0.1908885047012219,"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."}}