{"id":"W7116896472","doi":"10.26599/tst.2024.9010243","title":"From Model Parameters to Data Quality: Implicit Factor Evaluation of Model Extraction Attacks","year":2025,"lang":"en","type":"article","venue":"Tsinghua Science & Technology","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Quality (philosophy); Range (aeronautics); Key (lock); Annotation; Focus (optics); Deep learning; Data quality","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":[0.002894485,0.0001910056,0.0003155544,0.001494169,0.0003179531,0.0001367496,0.005304031,0.000197173,0.000005720117],"category_scores_gemma":[0.002720698,0.0001962691,0.00003645552,0.003924253,0.0005664367,0.001589242,0.002778344,0.0003818339,0.00001154908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000419701,"about_ca_system_score_gemma":0.001200323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003695177,"about_ca_topic_score_gemma":0.00004057737,"domain_scores_codex":[0.9965374,0.0001033048,0.0005158455,0.001285199,0.001086066,0.0004721232],"domain_scores_gemma":[0.9960501,0.0001614058,0.0003035091,0.00293092,0.0004783318,0.00007575436],"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":[0.000007608978,0.00003654567,0.0003895158,0.000004135194,0.000009073088,3.382957e-7,0.0005023296,0.7445893,0.1055697,0.02265418,0.0000563967,0.1261809],"study_design_scores_gemma":[0.0002237919,0.00002290683,0.0004899509,0.0000326491,0.00002291744,0.000001161586,0.0001459576,0.9066905,0.01475304,0.07742348,0.00002920542,0.0001644135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3464286,0.00002321371,0.6498527,0.002508516,0.0002734533,0.000234719,0.00001351131,0.0002382846,0.0004269661],"genre_scores_gemma":[0.6869401,0.00000149368,0.3128417,0.0001499235,0.000008558378,0.00001409162,0.000004372514,0.000005637508,0.00003407586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3405115,"threshold_uncertainty_score":0.9856297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1548274608443632,"score_gpt":0.4612913278443817,"score_spread":0.3064638670000185,"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."}}