{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001707419,0.0006779591,0.0005933741,0.0007071247,0.0003696679,0.0009018668,0.0009073045,0.0009715706,0.001621781],"category_scores_gemma":[0.007607061,0.0002427401,0.0005854196,0.0005322326,0.001797892,0.001792325,0.002192857,0.00124754,0.000321485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008441387,"about_ca_system_score_gemma":0.0003821724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000709828,"about_ca_topic_score_gemma":0.0004030994,"domain_scores_codex":[0.9981369,0.0005725517,0.00008124992,0.0003084268,0.0006385592,0.0002622652],"domain_scores_gemma":[0.9957289,0.002156646,0.0005957884,0.001125701,0.0002545254,0.0001385257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004457878,0.00009454926,0.001967996,0.00007095974,0.00007573678,0.0003764895,0.00009750741,0.8757147,0.02117552,0.04716332,0.001097328,0.05172001],"study_design_scores_gemma":[0.00001109502,0.00006905097,0.0004094888,0.000006693418,0.000008569981,0.0001052875,0.00001137816,0.9798029,0.006045025,0.01304477,0.0004735789,0.00001212778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1724301,0.0003256217,0.8197957,0.0004895169,0.0001193392,0.00009654275,0.000135274,0.001072023,0.00553599],"genre_scores_gemma":[0.9825784,0.0001124891,0.0152418,0.00007251692,0.00002087434,0.0000288258,0.00004539954,0.00002738307,0.001872377],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001707419,"threshold_uncertainty_score":0.009029806,"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."}}