{"id":"W4378374920","doi":"10.1016/j.ins.2023.119231","title":"Trusted fingerprint localization for multimedia devices based on blockchain","year":2023,"lang":"en","type":"article","venue":"Information Sciences","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Fingerprint (computing); Robustness (evolution); Blockchain; Upload; Vulnerability (computing); Scheme (mathematics); Computer security; Process (computing); Spoofing attack; Collusion; Subsequence; Data mining; Computer network; World Wide Web","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":[0.0008613034,0.00009105987,0.00007980944,0.0005883963,0.0004105284,0.0002660942,0.0006255699,0.00004410595,0.000002326713],"category_scores_gemma":[0.00014591,0.00007299506,0.00004814687,0.001596832,0.00008928143,0.001142286,0.00005914412,0.0000411376,0.00003406895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001632495,"about_ca_system_score_gemma":0.00005001808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006394772,"about_ca_topic_score_gemma":0.000002757713,"domain_scores_codex":[0.9989883,0.00002578987,0.0002525292,0.0001574797,0.0003546868,0.0002212417],"domain_scores_gemma":[0.9992613,0.0002491746,0.0001424248,0.0001975721,0.0001070581,0.00004246559],"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":[0.00003269278,0.00004045022,0.002781195,0.0000999683,0.000007128778,0.000001209344,0.004109787,0.4213349,0.0001114565,0.04673634,0.003525381,0.5212196],"study_design_scores_gemma":[0.0001681771,0.00009930352,0.001016263,0.00003251175,8.957469e-7,4.178428e-7,0.00006189734,0.9786692,0.003656298,0.004268683,0.01191903,0.0001073484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003119022,0.000002317785,0.9934156,0.0008486993,0.0002355618,0.0002870724,0.000005820994,0.0008434457,0.001242441],"genre_scores_gemma":[0.8973432,0.000002315625,0.1010336,0.001474463,0.00001972103,0.00009597593,0.00001659247,0.00000229282,0.00001187156],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8942242,"threshold_uncertainty_score":0.3157494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02743374244911958,"score_gpt":0.2920106493529994,"score_spread":0.2645769069038798,"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."}}