{"id":"W2977667117","doi":"10.24018/ejece.2019.3.5.125","title":"Image Forgery Detection Based on Deep Transfer Learning","year":2019,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering and Computer Science","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Canadian Bureau for International Education","keywords":"Transfer of learning; Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Machine learning; Image (mathematics); Pattern recognition (psychology); Computation; Feature (linguistics); Computer vision; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001031282,0.0001193339,0.00014379,0.0004615529,0.00007181161,0.0003252529,0.0004943386,0.00001360422,9.462348e-7],"category_scores_gemma":[0.0001095083,0.0001007716,0.00006581456,0.0009255707,0.00004243143,0.0007703338,0.00005431194,0.0003658377,0.00001857594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005055136,"about_ca_system_score_gemma":0.00004125617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.884705e-7,"about_ca_topic_score_gemma":2.064341e-8,"domain_scores_codex":[0.9987168,0.00005988375,0.0002451392,0.000247327,0.0004548953,0.0002759573],"domain_scores_gemma":[0.9993411,0.0001323217,0.00006082212,0.0001617308,0.0001243,0.0001796794],"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":[0.00001999491,0.0000381837,0.00004047227,0.00001216137,0.000006293539,0.00008367426,0.0001090126,0.1212357,0.03511022,0.0006266612,0.0000059871,0.8427116],"study_design_scores_gemma":[0.0002942705,0.001536582,0.002918477,0.00004072876,0.000002320072,0.0001737448,7.25345e-7,0.9871287,0.007376425,0.00001648414,0.0003922895,0.0001191896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.182971,0.00002020495,0.8157676,0.00004251525,0.0007488125,0.00003583552,2.675659e-8,0.00006364875,0.0003503501],"genre_scores_gemma":[0.9680418,0.000003002227,0.03173142,0.00008812552,0.0001185559,2.408006e-7,3.726265e-8,0.00001023514,0.000006587602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8658931,"threshold_uncertainty_score":0.4109348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003248629882343737,"score_gpt":0.1601588262725553,"score_spread":0.1569101963902116,"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."}}