{"id":"W4285601015","doi":"10.24963/ijcai.2022/433","title":"Online Evasion Attacks on Recurrent Models:The Power of Hallucinating the Future","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Office of Experimental Program to Stimulate Competitive Research; Ministry of Science and ICT, South Korea; Institute for Information and Communications Technology Promotion; National Science Foundation","keywords":"Computer science; Generality; Hallucinating; Robustness (evolution); Vulnerability (computing); Adversarial system; Computer security; Machine learning; Artificial intelligence","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":[0.001595218,0.0002852065,0.0002785686,0.0001882774,0.0008744713,0.0001766063,0.004958783,0.00006627374,0.0001938684],"category_scores_gemma":[0.0006554149,0.0001671289,0.0002469606,0.0006563897,0.0002761502,0.0003737436,0.002103493,0.001331383,0.00001110886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002097153,"about_ca_system_score_gemma":0.000114386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000569789,"about_ca_topic_score_gemma":0.00002764581,"domain_scores_codex":[0.9964015,0.0001027858,0.0008429238,0.0005588143,0.001790354,0.0003035471],"domain_scores_gemma":[0.9970554,0.0003535785,0.001117908,0.0005046102,0.0009165342,0.00005192887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001433719,0.0003544224,0.00005055404,0.00001644537,0.00003977201,9.719765e-7,0.004188723,0.08095767,0.0009719567,0.884087,0.0002445393,0.0289446],"study_design_scores_gemma":[0.00007249424,0.0004333016,0.0003534948,0.0002795939,0.0000163309,0.00001746056,0.005796434,0.8247341,0.01246754,0.1544035,0.001182962,0.0002427671],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4776554,0.0002382921,0.1961561,0.2805346,0.01607945,0.002576359,0.0001319586,0.0003411639,0.02628672],"genre_scores_gemma":[0.9969448,0.000043,0.001944939,0.0005939503,0.0002517364,0.00004675672,0.000002911241,0.00001902863,0.000152911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7437764,"threshold_uncertainty_score":0.9214734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07167116162927387,"score_gpt":0.3026082033544833,"score_spread":0.2309370417252094,"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."}}