{"id":"W2095765012","doi":"10.1109/mmsp.2008.4665170","title":"Fast Johnson-Lindenstrauss Transform for robust and secure image hashing","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hash function; Computer science; Robustness (evolution); K-independent hashing; Universal hashing; Dynamic perfect hashing; Singular value decomposition; Matrix decomposition; Random projection; Algorithm; Dimensionality reduction; Feature hashing; Non-negative matrix factorization; Artificial intelligence; Theoretical computer science; Hash table; Pattern recognition (psychology); Double hashing; Computer security","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":[0.0001347165,0.0001460866,0.0001635277,0.00006959231,0.0002471232,0.000110211,0.0003502178,0.00006779999,0.000008401085],"category_scores_gemma":[0.00002930977,0.00012196,0.00006686503,0.0001828497,0.00008788418,0.001366412,0.00006600571,0.0001278656,0.000003910162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001871947,"about_ca_system_score_gemma":0.00003441853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009316181,"about_ca_topic_score_gemma":0.000007244831,"domain_scores_codex":[0.9990287,0.00000934074,0.0001807498,0.0003460016,0.0001478769,0.0002872926],"domain_scores_gemma":[0.9994533,0.00007515481,0.00003896808,0.0002436677,0.00009259804,0.00009630188],"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":[0.00005899786,0.0001479052,0.0001449818,0.0001454594,0.00003966861,0.0001431915,0.002833094,0.0000387926,0.01012248,0.02749083,0.01347246,0.9453622],"study_design_scores_gemma":[0.002691443,0.0009709048,0.001149454,0.0001113634,0.00002461408,0.001025776,0.0002664819,0.09329246,0.799145,0.03345866,0.06646616,0.0013977],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001245032,0.0001750299,0.9922555,0.0008868523,0.00005496217,0.0003392224,0.000004853383,0.0004318806,0.00460664],"genre_scores_gemma":[0.1139963,0.000433689,0.8841125,0.00039189,0.00006667353,0.00002422682,0.000002478856,0.00001481768,0.0009574549],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9439644,"threshold_uncertainty_score":0.4973383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03165891914092377,"score_gpt":0.2747031836536726,"score_spread":0.2430442645127489,"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."}}