{"id":"W3136302944","doi":"10.1007/s12083-021-01091-9","title":"Obfuscation of images via differential privacy: From facial images to general images","year":2021,"lang":"en","type":"article","venue":"Peer-to-Peer Networking and Applications","topic":"Face recognition and analysis","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Obfuscation; Computer science; Differential privacy; Context (archaeology); Anonymity; Artificial intelligence; Pixel; Face (sociological concept); Noise (video); Generative grammar; Generative model; Machine learning; Image (mathematics); Computer vision; Computer security; Data mining","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":[0.0001647007,0.0001917058,0.0002820218,0.0001623573,0.0002666918,0.0003394705,0.0004795217,0.00006045477,0.00005332297],"category_scores_gemma":[0.00004589487,0.0001988914,0.0001086019,0.0009595542,0.00003617617,0.0001400176,0.0004099741,0.0001182179,0.00009816763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002678558,"about_ca_system_score_gemma":0.00004430806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000139472,"about_ca_topic_score_gemma":0.00002532741,"domain_scores_codex":[0.9981886,0.00006457614,0.000342083,0.000652268,0.0004553115,0.0002971181],"domain_scores_gemma":[0.9983969,0.00009431395,0.00009801438,0.0005672158,0.0005680241,0.0002755414],"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":[0.000008111461,0.0002250436,0.003347207,0.00001799233,0.0001147139,0.000003150702,0.0009861534,0.001515408,0.1563826,0.0009078903,0.02486427,0.8116274],"study_design_scores_gemma":[0.001274728,0.0001266989,0.1254709,0.0001990153,0.000390577,0.00002077308,0.0002834626,0.04353201,0.2750262,0.01303873,0.5387416,0.001895217],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03746298,0.000149617,0.9450922,0.01605682,0.0001862279,0.0002887226,0.000138435,0.0001197166,0.0005052735],"genre_scores_gemma":[0.8949327,0.00005364224,0.09988163,0.0007413085,0.0009198804,0.0002542024,0.0002641825,0.00001947062,0.002933014],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8574697,"threshold_uncertainty_score":0.8110557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430885476689927,"score_gpt":0.2645212001815003,"score_spread":0.250212345414601,"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."}}