{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006772152,0.0007067635,0.0006637628,0.0003218278,0.0004211375,0.0008343025,0.001390826,0.001191712,0.007003404],"category_scores_gemma":[0.002559915,0.0004928955,0.0006538825,0.0002321001,0.001228895,0.002071411,0.003634402,0.001989264,0.001878129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003347585,"about_ca_system_score_gemma":0.0002741228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001860843,"about_ca_topic_score_gemma":0.0004156659,"domain_scores_codex":[0.999185,0.0002540247,0.00002856075,0.0001232805,0.0003208556,0.00008819996],"domain_scores_gemma":[0.9987504,0.0005909422,0.00006722592,0.0004557538,0.00008624623,0.00004941089],"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":[0.0007384429,0.0002993958,0.0007138018,0.00031942,0.0001625841,0.0006551975,0.0003694005,0.3939871,0.0835362,0.1528879,0.01375816,0.3525725],"study_design_scores_gemma":[0.00005554613,0.0001460172,0.0001286614,0.00002854894,0.00002219364,0.0002608143,0.00002743881,0.9075536,0.02288303,0.06333362,0.005526277,0.00003428089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02783477,0.000211833,0.9611102,0.0002082565,0.0001793527,0.0001233271,0.00007365713,0.002855639,0.007402946],"genre_scores_gemma":[0.6276599,0.000226704,0.3510478,0.000399945,0.00009936462,0.000299742,0.0003073308,0.001104376,0.01885478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007003404,"threshold_uncertainty_score":0.02342868,"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."}}