{"id":"W4401407925","doi":"10.18280/rces.110202","title":"Enhancing Steganography in 256×256 Colored Images with U-Net: A Study on PSNR and SSIM Metrics with Variable-Sized Hidden Images","year":2024,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Steganography; Artificial intelligence; Colored; Variable (mathematics); Computer science; Pattern recognition (psychology); Computer vision; Mathematics; Image (mathematics); Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008191945,0.0006909526,0.0003673914,0.0005162364,0.0001061369,0.0003918544,0.0003814762,0.0004546276,0.0004975321],"category_scores_gemma":[0.001599179,0.0001319646,0.0002976354,0.0003165072,0.0004417643,0.000686774,0.0003564004,0.0003714527,0.0001284562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003881867,"about_ca_system_score_gemma":0.0002707124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001581751,"about_ca_topic_score_gemma":0.001450749,"domain_scores_codex":[0.9998281,0.0000445263,0.00001001322,0.000030269,0.00005965897,0.00002730715],"domain_scores_gemma":[0.9996009,0.0002064734,0.00005617989,0.00003352717,0.00008694295,0.00001593677],"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":[0.000664563,0.0002161444,0.003528058,0.0004005896,0.0001196815,0.0003677102,0.0001200279,0.6193987,0.09634088,0.004750052,0.001071572,0.2730219],"study_design_scores_gemma":[0.000003900448,0.0002254921,0.001105147,0.00001879928,0.00001688103,0.0001090284,0.00002149719,0.9590556,0.03849066,0.0005369498,0.0004072607,0.000008781382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6138443,0.003437494,0.3750586,0.0003545,0.0001626748,0.00007616506,0.0001449206,0.0008655695,0.006055923],"genre_scores_gemma":[0.9454545,0.0009276855,0.05162264,0.00006565373,0.00002121752,0.00002390127,0.0001171161,0.00004390754,0.001723376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001581751,"threshold_uncertainty_score":0.004332364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01072821502041783,"score_gpt":0.2637298068388503,"score_spread":0.2530015918184325,"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."}}