{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01674156,0.001508175,0.001075493,0.00110082,0.0005403156,0.001616455,0.0009961388,0.001470784,0.001098953],"category_scores_gemma":[0.1063382,0.0003962987,0.0007142873,0.0006312949,0.002265914,0.003639186,0.002301418,0.002724398,0.0002213383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00160687,"about_ca_system_score_gemma":0.001156852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001612994,"about_ca_topic_score_gemma":0.001272578,"domain_scores_codex":[0.9907879,0.00515137,0.0005567361,0.0008958908,0.002067315,0.0005407341],"domain_scores_gemma":[0.8932444,0.0822528,0.005845452,0.01316293,0.004364915,0.001129472],"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":[0.001566164,0.0002224427,0.01421157,0.0002471106,0.00026839,0.0001514717,0.0001316149,0.907728,0.006628472,0.01196353,0.001514472,0.0553668],"study_design_scores_gemma":[0.00002475106,0.00024875,0.001121014,0.00003307731,0.00002604342,0.00006268523,0.00003406986,0.9873518,0.005234836,0.005605428,0.0002377164,0.00001985845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5816902,0.001869479,0.4090821,0.001681531,0.0001726918,0.0002479392,0.0003651864,0.001387451,0.003503465],"genre_scores_gemma":[0.9758601,0.0001144992,0.02342485,0.0001034183,0.00002289028,0.00003427858,0.0001423347,0.00007247784,0.0002251884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01674156,"threshold_uncertainty_score":0.08853889,"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."}}