{"id":"W4200333262","doi":"10.1016/j.future.2021.11.032","title":"Differentially private facial obfuscation via generative adversarial networks","year":2021,"lang":"en","type":"article","venue":"Future Generation Computer Systems","topic":"Face recognition and analysis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obfuscation; Computer science; Task (project management); Adversarial system; Computer security; Human–computer interaction; Process (computing); Artificial intelligence; Machine learning","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":[0.001147553,0.000929885,0.0008594671,0.0004107714,0.0004594344,0.0007759195,0.0009409367,0.001536448,0.003089517],"category_scores_gemma":[0.004535167,0.0004656068,0.0008782533,0.0003160534,0.00173091,0.001663443,0.003471763,0.002442853,0.0009296808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007829257,"about_ca_system_score_gemma":0.0006731536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009424588,"about_ca_topic_score_gemma":0.001193551,"domain_scores_codex":[0.9988942,0.0003073859,0.00003385057,0.0002038747,0.0003788069,0.0001820524],"domain_scores_gemma":[0.9978728,0.001297741,0.000123148,0.0005571645,0.0001018517,0.00004732336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005851009,0.0001203785,0.001253563,0.0001302002,0.0001137521,0.0004348714,0.0001693814,0.6823326,0.01942744,0.1404879,0.004403301,0.1505414],"study_design_scores_gemma":[0.00001729675,0.00003269724,0.0001844974,0.00001769157,0.00001759996,0.0001000953,0.00001106507,0.9497604,0.0050804,0.04402288,0.0007425226,0.00001273843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03638918,0.0004018661,0.9561367,0.0008119885,0.0001188856,0.00005074295,0.0001435472,0.0009020125,0.005045094],"genre_scores_gemma":[0.9141511,0.0003452767,0.07675839,0.0002825804,0.0000904625,0.0000849956,0.0001967558,0.0000938645,0.007996547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003089517,"threshold_uncertainty_score":0.01033545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01309810059167782,"score_gpt":0.2117237539068038,"score_spread":0.198625653315126,"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."}}