{"id":"W4288287132","doi":"10.48550/arxiv.1907.03038","title":"Faking and Discriminating the Navigation Data of a Micro Aerial Vehicle\\n Using Quantum Generative Adversarial Networks","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Discriminator; Computer science; Covert; Adversary; Point (geometry); Generator (circuit theory); Artificial intelligence; Quantum; Software; Computer engineering; Human–computer interaction; Computer security; Mathematics","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":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001940926,0.0007874436,0.0009568832,0.0002494934,0.001195215,0.000473301,0.004964951,0.0008251884,0.00001959985],"category_scores_gemma":[0.0003856555,0.0007901757,0.0002407019,0.001053669,0.0008983245,0.001940719,0.01579164,0.00275664,0.000005126511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003646634,"about_ca_system_score_gemma":0.0006202593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001887142,"about_ca_topic_score_gemma":0.00004611283,"domain_scores_codex":[0.9936953,0.001658011,0.0008467321,0.002690067,0.000311884,0.0007980025],"domain_scores_gemma":[0.9929224,0.001056019,0.00224193,0.00319466,0.0004122439,0.0001727831],"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":[0.0002383538,0.00005850336,0.003507926,0.0001755044,0.0002618864,0.00007522275,0.002736667,0.9508317,0.001019324,0.03981295,0.0000107775,0.001271216],"study_design_scores_gemma":[0.001556947,0.000095954,0.0009890583,0.0008990564,0.0006065861,0.00002105316,0.001387761,0.9900697,0.0001031418,0.003471205,0.00002756418,0.0007719068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4071592,0.0001228586,0.5891295,0.00007143144,0.002763342,0.0006050732,0.00003860579,0.00004013086,0.00006983832],"genre_scores_gemma":[0.9884894,0.0001565664,0.01000198,0.00004834729,0.00106945,5.664489e-7,0.0001223805,0.00005854345,0.00005278578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5813302,"threshold_uncertainty_score":0.999544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1329305008469713,"score_gpt":0.249463978792771,"score_spread":0.1165334779457998,"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."}}