{"id":"W4362500047","doi":"10.1007/978-3-031-29504-1_11","title":"HoneyGAN: Creating Indistinguishable Honeywords with Improved Generative Adversarial Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Password; Computer science; Metric (unit); Adversarial system; Representation (politics); Task (project management); Generative grammar; Computer security; Artificial intelligence; Theoretical computer science; Machine learning","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001065208,0.0006029803,0.000651019,0.0006706705,0.0005458415,0.001166869,0.002882141,0.0003706606,0.00001293163],"category_scores_gemma":[0.0001687897,0.0005143874,0.0001166647,0.001180456,0.0005289114,0.000535015,0.00114104,0.0009253089,0.00004999779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002729748,"about_ca_system_score_gemma":0.0007212743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001203545,"about_ca_topic_score_gemma":0.0004539007,"domain_scores_codex":[0.9957106,0.00006585892,0.000629242,0.001762066,0.001005028,0.000827233],"domain_scores_gemma":[0.9969071,0.0005289005,0.0005044321,0.001401555,0.0004147124,0.0002433616],"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":[0.0001307102,0.0002668048,0.00123715,0.0003906536,0.0004122045,0.0009722004,0.1430023,0.1528395,0.0006281327,0.1689298,0.0004460922,0.5307444],"study_design_scores_gemma":[0.0004416334,0.0002044193,0.00009827918,0.0004126634,0.00001266686,0.00004029227,0.000001091115,0.9778201,0.0001890463,0.01968682,0.0004152016,0.0006777984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0000964322,0.0001125164,0.9928967,0.0003937682,0.003079559,0.0006155571,0.000006955236,0.0004317643,0.002366715],"genre_scores_gemma":[0.663438,0.00003661857,0.3256929,0.001909108,0.004066177,0.00007779899,0.0000566737,0.0001847641,0.004537895],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8249806,"threshold_uncertainty_score":0.99987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152147343483243,"score_gpt":0.2321677936146981,"score_spread":0.2169530592663738,"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."}}