{"id":"W4413639965","doi":"10.1109/compsac65507.2025.00186","title":"Towards Explainable AI in Continuous Smartphone Authentication: Leveraging CNN, BiLSTM, and Attention Techniques","year":2025,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Authentication (law); Computer security; 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":[0.0002968583,0.0000979198,0.0001316382,0.0003133735,0.00005441124,0.0002658702,0.0002115491,0.0000505257,0.000004901434],"category_scores_gemma":[0.00007172536,0.00009282375,0.00002461401,0.0006193384,0.00004803991,0.0008656318,0.0002009071,0.00009007991,0.00000945097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008136653,"about_ca_system_score_gemma":0.00005124911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001522606,"about_ca_topic_score_gemma":0.0000345527,"domain_scores_codex":[0.9991058,0.00002751208,0.0002137527,0.0003200086,0.0001421229,0.0001907794],"domain_scores_gemma":[0.9995207,0.00003289293,0.00003859457,0.0002894611,0.00008089458,0.00003751082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006317598,0.00006482896,0.003044266,0.00003824022,0.000009070756,0.00001001898,0.0003812646,5.891332e-7,0.005133568,0.05361498,0.001568504,0.9361284],"study_design_scores_gemma":[0.002270005,0.0004340352,0.2307874,0.0008339242,0.00003310252,0.0001045647,0.001049472,0.06074055,0.3465351,0.3096225,0.04644626,0.00114311],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1883781,0.00008185662,0.7667597,0.00510033,0.0006531689,0.000359132,2.881579e-7,0.000531422,0.03813596],"genre_scores_gemma":[0.978765,0.000009586244,0.01671143,0.0005373974,0.00001734186,0.00005747653,0.000001566324,0.000004270596,0.003895951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9349852,"threshold_uncertainty_score":0.3785243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007570850129353855,"score_gpt":0.2386055329685471,"score_spread":0.2310346828391933,"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."}}