{"id":"W2957341760","doi":"10.18280/ts.360114","title":"Frequency Domain Steganography with Reversible Texture Combination","year":2019,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Steganography; Texture (cosmology); Computer science; Artificial intelligence; Frequency domain; Pattern recognition (psychology); Domain (mathematical analysis); Speech recognition; Mathematics; Computer vision; Embedding; Image (mathematics); Mathematical analysis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002543734,0.0001917032,0.0001668398,0.0002559097,0.0001340119,0.000115758,0.0006515355,0.00006583863,0.00007649152],"category_scores_gemma":[9.060491e-7,0.0001520515,0.00008988976,0.000659047,0.00005304228,0.0008880158,0.00006888066,0.0001601284,0.00002040248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003115177,"about_ca_system_score_gemma":0.00002720171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007573506,"about_ca_topic_score_gemma":0.000003458963,"domain_scores_codex":[0.9986466,0.0000685477,0.0002002753,0.0003930856,0.0003890385,0.0003024239],"domain_scores_gemma":[0.9992946,0.00003443102,0.0001160901,0.0004026695,0.00008135158,0.00007084458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009114519,0.0005204382,0.09405563,0.0001273884,0.000150798,0.00006602564,0.002336284,0.00008688395,0.02675775,0.8604773,0.00181431,0.01351601],"study_design_scores_gemma":[0.00927648,0.005932416,0.07589821,0.0008176802,0.00007872388,0.0001465517,0.0005208403,0.004552385,0.05337533,0.8203399,0.02618413,0.002877385],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1897651,0.0000723347,0.8001396,0.0004890451,0.0001210614,0.0005589527,0.000004467157,0.0005179167,0.008331543],"genre_scores_gemma":[0.8741378,0.00000788232,0.1254072,0.0003079925,0.00002396373,0.00002860496,0.0000111247,0.00001039035,0.00006513539],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6843727,"threshold_uncertainty_score":0.6200479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006104084143149079,"score_gpt":0.20102417253787,"score_spread":0.1949200883947209,"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."}}