{"id":"W6901674300","doi":"10.60692/cecfd-89t90","title":"IMG‐forensics: Multimedia‐enabled information hiding investigation using convolutional neural network","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Information hiding; Steganography; Encryption; Robustness (evolution); Cover (algebra); Least significant bit; Cryptography; Convolutional neural network","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":[0.0004649113,0.0002360356,0.0002471569,0.0002964871,0.0004465627,0.0006309753,0.0003560032,0.0001530076,0.000003647886],"category_scores_gemma":[0.00003770663,0.0002242182,0.0001162771,0.0008531876,0.0000554459,0.008100603,0.0002307548,0.0001760743,0.00006486294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001832588,"about_ca_system_score_gemma":0.0001214859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006137092,"about_ca_topic_score_gemma":1.45754e-7,"domain_scores_codex":[0.9979506,0.0001322508,0.0008549054,0.000192064,0.0004570618,0.0004131826],"domain_scores_gemma":[0.9982364,0.00002093617,0.0005479209,0.0004886624,0.0005772125,0.0001288356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001834755,0.00001095367,0.4565251,0.002252031,0.0003975873,0.00007595082,0.2341185,0.1618762,0.0002374692,0.1145301,0.002374872,0.02741781],"study_design_scores_gemma":[0.0007856247,0.00002603179,0.01170032,0.0003013795,0.00002054352,0.000224003,0.00146448,0.9793802,0.00456696,0.0004271009,0.0006670085,0.0004363109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1158122,0.000007943616,0.8814969,0.00008591006,0.0009653601,0.0002822084,0.00002259116,0.000814848,0.0005119786],"genre_scores_gemma":[0.8506483,2.818549e-7,0.1485917,0.0004771753,0.0001383791,0.00003276659,0.00009691727,0.000006128282,0.000008409926],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8175041,"threshold_uncertainty_score":0.9143354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441775757706392,"score_gpt":0.2165224313501087,"score_spread":0.1821046737730448,"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."}}