{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001123016,0.0005554566,0.0003913575,0.0002847228,0.0003839944,0.0007761221,0.0007476438,0.0009601315,0.001354884],"category_scores_gemma":[0.004217374,0.0002075472,0.0004349921,0.0002377724,0.002073445,0.001011556,0.001565752,0.001090349,0.0002015592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009071814,"about_ca_system_score_gemma":0.0006166212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001538508,"about_ca_topic_score_gemma":0.001396998,"domain_scores_codex":[0.999317,0.0002654975,0.00002017541,0.0001203722,0.0001688559,0.000108116],"domain_scores_gemma":[0.9973001,0.001900841,0.000300658,0.0003094809,0.0001256547,0.00006323911],"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":[0.0002306406,0.00004495844,0.001484218,0.00006037571,0.00004518406,0.0001579315,0.00009474342,0.9107929,0.007006251,0.05692128,0.0008135214,0.02234782],"study_design_scores_gemma":[0.000006560394,0.00003422191,0.0001141404,0.000004923368,0.000005951971,0.00002333326,0.000007903744,0.9862705,0.002520862,0.01071311,0.00029191,0.000006625146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2388262,0.0002665888,0.7503879,0.00138395,0.0000901861,0.00008181716,0.0001100766,0.0004566194,0.008396575],"genre_scores_gemma":[0.9744751,0.00008654052,0.02327123,0.0001477193,0.00001656858,0.00003158697,0.00005094499,0.00002028358,0.001900059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001538508,"threshold_uncertainty_score":0.006582081,"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."}}