{"id":"W2023425418","doi":"10.1109/tifs.2013.2264462","title":"Extracting Spread-Spectrum Hidden Data From Digital Media","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Eion (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; University at Buffalo","keywords":"Computer science; Spread spectrum; Embedding; Host (biology); Autocorrelation; Algorithm; Autocorrelation matrix; Artificial intelligence; Channel (broadcasting); Mathematics; Telecommunications; Statistics","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":[0.0003271003,0.0005331803,0.0004707669,0.0005418438,0.0002336276,0.0005542561,0.0005620158,0.0008950432,0.0006153854],"category_scores_gemma":[0.001578858,0.0002194837,0.0003417563,0.0003830036,0.001116505,0.00142008,0.0009564939,0.0005300311,0.0003687274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001769461,"about_ca_system_score_gemma":0.0003535656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003005207,"about_ca_topic_score_gemma":0.0003302094,"domain_scores_codex":[0.9998173,0.00002669358,0.000009836336,0.00003711087,0.00009305332,0.00001598303],"domain_scores_gemma":[0.9995006,0.0002495764,0.0000877542,0.00008918741,0.00005574688,0.0000170461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004048891,0.0001288925,0.001474796,0.0005291594,0.00007654239,0.001070062,0.0003809857,0.2275398,0.3912727,0.03767702,0.0006120104,0.3388331],"study_design_scores_gemma":[0.00002564772,0.0001783766,0.0005627515,0.00002661465,0.00002477588,0.0005825348,0.000134078,0.7726594,0.2044489,0.01939315,0.00192503,0.00003879088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08263993,0.000248437,0.9161254,0.0001521711,0.00003194048,0.00002510221,0.00003889757,0.0001240204,0.000614172],"genre_scores_gemma":[0.5911421,0.0007374017,0.4045404,0.00007193329,0.00008969591,0.00004056672,0.000140034,0.00003535179,0.00320258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008950432,"threshold_uncertainty_score":0.002058685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02086315380313131,"score_gpt":0.2386269608509912,"score_spread":0.2177638070478599,"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."}}