{"id":"W3005857660","doi":"10.1109/icb45273.2019.8987267","title":"Directed Adversarial Attacks on Fingerprints using Attributions","year":2019,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Fingerprint (computing); Minutiae; Computer science; Artificial intelligence; Fingerprint recognition; Pattern recognition (psychology); Fingerprint Verification Competition; Biometrics; Matching (statistics); Artificial neural network; Noise (video); Data mining; Mathematics; Image (mathematics); Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001795017,0.0000663459,0.00008234271,0.0002208237,0.0000936916,0.0001178947,0.000409604,0.00005569971,0.0004164002],"category_scores_gemma":[0.00006287315,0.00006073246,0.00004819292,0.001116361,0.00001435964,0.0002038966,0.0001229577,0.00008543973,0.001515248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007161537,"about_ca_system_score_gemma":0.00005114482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007036259,"about_ca_topic_score_gemma":0.000003456698,"domain_scores_codex":[0.9991818,0.00004184601,0.0001291974,0.0002734934,0.0002176572,0.0001560025],"domain_scores_gemma":[0.9992813,0.00006793388,0.00004432776,0.0004617628,0.0000863223,0.00005832009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003753296,0.0009571469,0.01560482,0.00003257359,0.0001062843,0.00001603453,0.001023926,0.0005499009,0.01963326,0.8916469,0.02447542,0.04591624],"study_design_scores_gemma":[0.00163972,0.0001078216,0.1190904,0.00003287907,0.00001311841,0.0000195253,0.00003538177,0.7285295,0.01711555,0.001702572,0.1310285,0.000684985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3510971,0.00001414461,0.6246531,0.0008447169,0.002700263,0.0002566046,0.000007988921,0.0005761876,0.01984989],"genre_scores_gemma":[0.9851368,0.000001397352,0.01233593,0.000259706,0.00003690831,0.000001392715,0.000005770245,0.000002877328,0.002219192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8899443,"threshold_uncertainty_score":0.9992622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03428365359289313,"score_gpt":0.2818174233070218,"score_spread":0.2475337697141286,"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."}}