{"id":"W4394881831","doi":"10.1145/3625468.3647611","title":"FlexMark","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Robustness (evolution); Generality; Embedding; Discriminator; Digital watermarking; Image quality; Artificial intelligence; Image (mathematics); Computer engineering; Data mining; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007800288,0.00113359,0.0007009472,0.001426475,0.00056503,0.00185438,0.003181938,0.001740424,0.05269838],"category_scores_gemma":[0.003516255,0.0005036269,0.0007047555,0.001262761,0.00056362,0.004517592,0.002832022,0.0009793204,0.03596482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004507066,"about_ca_system_score_gemma":0.000917675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001121334,"about_ca_topic_score_gemma":0.001660771,"domain_scores_codex":[0.9993129,0.00005900749,0.00005554592,0.0001647365,0.0003302536,0.00007760079],"domain_scores_gemma":[0.9987299,0.0002300826,0.0001104239,0.0006095504,0.0002505828,0.00006945128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001341255,0.0002535692,0.001626855,0.001241981,0.0000993443,0.0004421326,0.0001932446,0.01108404,0.0300766,0.02845173,0.1881364,0.7370529],"study_design_scores_gemma":[0.0002251125,0.0004740519,0.001702842,0.0001923454,0.00007152698,0.001745678,0.0001332691,0.1124487,0.08641127,0.03073741,0.7656869,0.0001707935],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.03329939,0.006400996,0.5194949,0.001120296,0.001464431,0.0008793437,0.01692345,0.2412928,0.1791244],"genre_scores_gemma":[0.2585058,0.00470152,0.4413548,0.00198867,0.0004505833,0.001194258,0.06388257,0.01556465,0.2123573],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05269838,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01146978046067611,"score_gpt":0.2603271930075415,"score_spread":0.2488574125468654,"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."}}