{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008271655,0.0007574066,0.000704994,0.001011107,0.0002999999,0.0005595925,0.00133054,0.000842324,0.001094052],"category_scores_gemma":[0.001872719,0.0002998938,0.0005898311,0.0004472174,0.000535075,0.001372467,0.001009851,0.0009588192,0.0004660069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008795848,"about_ca_system_score_gemma":0.0006397238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003012267,"about_ca_topic_score_gemma":0.002260746,"domain_scores_codex":[0.9996552,0.00005480364,0.00001738844,0.0000911202,0.000120819,0.0000606518],"domain_scores_gemma":[0.9993813,0.0001940702,0.0001031518,0.000102311,0.0001842287,0.00003498315],"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":[0.0003448276,0.0003170278,0.004527374,0.00007428529,0.0001371501,0.0002341396,0.0001047024,0.4111252,0.02214847,0.002660112,0.001715434,0.5566112],"study_design_scores_gemma":[0.00000202259,0.00003793627,0.0003525757,0.000003117261,0.000006382909,0.00003646939,0.000005519998,0.9934477,0.005263109,0.0007195697,0.0001205142,0.000005085861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1949693,0.0006334849,0.7982697,0.0003017212,0.00006794729,0.00009637642,0.00006944434,0.002549137,0.003042851],"genre_scores_gemma":[0.937492,0.0001991397,0.0591364,0.0001192599,0.00002309066,0.00004592727,0.000127023,0.00003808006,0.002818959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003012267,"threshold_uncertainty_score":0.006381869,"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."}}