{"id":"W2289706180","doi":"10.1109/icitst.2015.7412065","title":"Fingerprint security for protecting EMV payment cards","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer security; Payment; Identity theft; Computer science; Fingerprint (computing); Biometrics; Authentication (law); Eavesdropping; Counterfeit; Internet privacy; Credit card; Payment card; Issuing bank; Countermeasure; Phishing; The Internet; World Wide Web; Engineering","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":[0.0007166274,0.0003800521,0.0004024129,0.0008519114,0.0004537388,0.001257753,0.0007179097,0.001290988,0.002243194],"category_scores_gemma":[0.001309518,0.0002285886,0.00027878,0.000755008,0.0003925409,0.001726574,0.0007988692,0.0005716115,0.0009830737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003755025,"about_ca_system_score_gemma":0.0002654952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002135758,"about_ca_topic_score_gemma":0.0001494132,"domain_scores_codex":[0.9989755,0.0002429973,0.00006350303,0.0001223132,0.000447509,0.0001481397],"domain_scores_gemma":[0.9993535,0.0001389514,0.0001065539,0.0002176683,0.0001576941,0.00002565905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001008175,0.0001073155,0.00682314,0.0004504694,0.00005107479,0.0005789909,0.0002087206,0.005057367,0.4953551,0.02706879,0.002721593,0.4605693],"study_design_scores_gemma":[0.00006605452,0.001538245,0.01311882,0.0002706374,0.0001778682,0.007348865,0.0003381055,0.070649,0.8341424,0.006527024,0.06568117,0.0001418238],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3753203,0.00957574,0.5907598,0.0008092594,0.0005307505,0.0001772669,0.0003135854,0.001665139,0.02084816],"genre_scores_gemma":[0.9336581,0.002221273,0.05801091,0.0001350281,0.00007149298,0.00002931305,0.0001082988,0.00002284939,0.005742826],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002243194,"threshold_uncertainty_score":0.007504225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06382096003522034,"score_gpt":0.3001066567977884,"score_spread":0.236285696762568,"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."}}