{"id":"W2922342173","doi":"10.1109/tip.2019.2904267","title":"Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Stony Brook University; Simon Fraser University; Government of Canada","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Context (archaeology); Adversarial system; RGB color model; Computer vision; Pattern recognition (psychology); Shadow (psychology); Artificial neural network; Image processing; Contextual image classification; Image (mathematics)","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":[0.0005688437,0.001015701,0.0006172138,0.0005150018,0.0002470686,0.0004355863,0.001144581,0.0007657821,0.001452249],"category_scores_gemma":[0.0009366357,0.0003950463,0.0006055364,0.0003281107,0.00047121,0.0007223502,0.001008564,0.0009611946,0.000474704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007725279,"about_ca_system_score_gemma":0.0005277374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005173708,"about_ca_topic_score_gemma":0.007397148,"domain_scores_codex":[0.9997372,0.00003850347,0.000009787482,0.00009723349,0.00007092335,0.00004637562],"domain_scores_gemma":[0.9997669,0.00006533454,0.00003341633,0.00006914985,0.00005193789,0.00001330567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005628595,0.0001790782,0.002320095,0.0001301148,0.0001778489,0.0003284784,0.00008216118,0.5566468,0.04110745,0.003364504,0.00394119,0.3911594],"study_design_scores_gemma":[0.000003263903,0.0000255888,0.000262887,0.000004632717,0.000009558497,0.0000347989,0.000003232049,0.9941074,0.00484687,0.0004491115,0.0002493574,0.000003422506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1703718,0.0009465747,0.8192927,0.0003606729,0.0001742776,0.000121156,0.0002646326,0.003731613,0.00473654],"genre_scores_gemma":[0.8618965,0.0004052219,0.1300534,0.0002631629,0.00005243548,0.00006803643,0.0005117749,0.0001073781,0.00664227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005173708,"threshold_uncertainty_score":0.01028723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891916739155991,"score_gpt":0.2269450545083561,"score_spread":0.2080258871167962,"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."}}