{"id":"W4396918594","doi":"10.1109/isdfs60797.2024.10527295","title":"Multimedia Forensics: Preserving Video Integrity with Blockchain","year":2024,"lang":"en","type":"article","venue":"","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"University of Winnipeg","keywords":"Computer science; Blockchain; Credibility; Video quality; Data integrity; Hash function; Process (computing); Video tracking; Trustworthiness; Video processing; Multimedia; Computer security; Computer hardware","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002230837,0.000379213,0.0004586689,0.0009718626,0.0009737533,0.001098943,0.001004264,0.001211691,0.003474651],"category_scores_gemma":[0.00509738,0.0002376756,0.0002342397,0.0009384745,0.0009949665,0.003268844,0.001874408,0.0006809998,0.000794258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004820323,"about_ca_system_score_gemma":0.000995977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079024,"about_ca_topic_score_gemma":0.000661739,"domain_scores_codex":[0.9986032,0.000421906,0.00005514862,0.000141106,0.0006209346,0.0001578288],"domain_scores_gemma":[0.9976593,0.0008690562,0.0002403986,0.0007740401,0.0003344764,0.0001228303],"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":[0.001498884,0.0003780357,0.003521033,0.0004158666,0.00008220966,0.001184099,0.0007792816,0.1292986,0.1606357,0.07557045,0.00409539,0.6225405],"study_design_scores_gemma":[0.0001939501,0.0007824032,0.0009789544,0.00009675534,0.00004226476,0.001401513,0.0002644284,0.7327546,0.1869247,0.0565607,0.01991491,0.00008481978],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1626819,0.001074481,0.8237771,0.0009201986,0.0001387186,0.0003311033,0.000166691,0.001886102,0.009023699],"genre_scores_gemma":[0.8570715,0.000558044,0.1373597,0.0001070621,0.0000743175,0.0000906912,0.0001319303,0.00007623517,0.00453042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003474651,"threshold_uncertainty_score":0.01179796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273550244349285,"score_gpt":0.2317521052785587,"score_spread":0.2190166028350659,"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."}}