{"id":"W3206880386","doi":"10.1145/3474085.3475591","title":"DAWN","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Digital watermarking; Adversary; Watermark; Embedding; Artificial intelligence; Adversarial system; Machine learning; Insider threat; Set (abstract data type); Computer security; Surrogate model; Data mining; Image (mathematics); Insider; Law","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":[0.001471379,0.0008792748,0.0005361026,0.000755991,0.0005635542,0.002092372,0.002236999,0.001449306,0.07537915],"category_scores_gemma":[0.005255002,0.0005228776,0.0006797921,0.000500963,0.000698959,0.003424738,0.003426629,0.002259999,0.03752989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009685949,"about_ca_system_score_gemma":0.001549479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002009733,"about_ca_topic_score_gemma":0.003224239,"domain_scores_codex":[0.9988272,0.0001714411,0.0000621381,0.0003348707,0.0004952492,0.0001091485],"domain_scores_gemma":[0.9981006,0.0004143601,0.0001135944,0.0009412341,0.0003024575,0.0001277913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006564932,0.0002753761,0.003073806,0.0005803669,0.0001237748,0.0003310403,0.000186746,0.0405558,0.01545438,0.1318696,0.2994179,0.5074747],"study_design_scores_gemma":[0.0001011579,0.0001592919,0.0008087714,0.00009832642,0.00002617649,0.0004317151,0.00005419712,0.2244154,0.02052381,0.07617439,0.6771393,0.00006755201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0155785,0.001800034,0.6386022,0.004052408,0.001806179,0.0006034547,0.01068074,0.1110113,0.2158652],"genre_scores_gemma":[0.28381,0.002826432,0.3820119,0.003439154,0.000574342,0.0008937982,0.0380922,0.01235888,0.2759933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07537915,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0088661727294761,"score_gpt":0.2420518776041695,"score_spread":0.2331857048746935,"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."}}