{"id":"W4405433123","doi":"10.1109/iccv51701.2025.00965","title":"FaceShield: Defending Facial Image Against Deepfake Threats","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Image (mathematics); Computer science; Environmental science; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002243426,0.0003243691,0.0004161254,0.0004474737,0.0001720617,0.0007035505,0.001373354,0.0002897288,0.0001829878],"category_scores_gemma":[0.00004499902,0.000299149,0.0004206097,0.0004464711,0.00004012725,0.0002570142,0.002196473,0.0005419036,0.0003533878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008101993,"about_ca_system_score_gemma":0.0001928965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004893572,"about_ca_topic_score_gemma":0.0001022902,"domain_scores_codex":[0.9980011,0.00008217609,0.0003601468,0.0008606338,0.0003415453,0.0003543967],"domain_scores_gemma":[0.9987327,0.00007310331,0.0001406277,0.0007930152,0.0001326819,0.0001278594],"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":[0.000003664211,0.0001273116,0.0004299819,0.000231971,0.000345289,0.00006444852,0.0007431213,0.001291386,0.0005020789,0.01833386,0.005667967,0.9722589],"study_design_scores_gemma":[0.001540026,0.00005867925,0.0006606688,0.001228771,0.0003658242,0.00001209703,0.0007015919,0.8380263,0.02627217,0.09750718,0.02980271,0.003823995],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00166172,0.0001742241,0.7814363,0.001337396,0.0008074054,0.0001837687,0.00003972405,0.0005194379,0.21384],"genre_scores_gemma":[0.4151973,0.002836511,0.5030868,0.00699854,0.0005371592,0.0002060131,0.0004366161,0.000047132,0.07065393],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9684349,"threshold_uncertainty_score":0.9999461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0301788413855438,"score_gpt":0.2947318044107394,"score_spread":0.2645529630251956,"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."}}