{"id":"W2767146254","doi":"10.5539/cis.v10n4p60","title":"A Linguistic Steganalysis Approach Base on Source Features of Text and Immune Mechanism","year":2017,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jiangsu Provincial Department of Education; Government of Jiangsu Province","keywords":"Steganalysis; Computer science; Steganography; Artificial intelligence; Natural language processing; Syntax; Authorship attribution; Linguistics; Embedding","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":[0.0003806796,0.0004021655,0.0004554505,0.001250332,0.000332682,0.000564087,0.0004790591,0.0006547206,0.0007647193],"category_scores_gemma":[0.0009282584,0.0001801374,0.000677494,0.0003593846,0.0006582936,0.001350936,0.0005818873,0.000660074,0.0003841174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002597146,"about_ca_system_score_gemma":0.0003404416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003147973,"about_ca_topic_score_gemma":0.0004485246,"domain_scores_codex":[0.9996166,0.0000641689,0.0000244153,0.00009299162,0.0001650041,0.00003670177],"domain_scores_gemma":[0.9995555,0.0001228473,0.00006839817,0.0000663783,0.0001677888,0.0000190198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002271571,0.0002345519,0.002992238,0.0003410995,0.0001684198,0.0004806595,0.0004709844,0.01898362,0.4801852,0.0309693,0.002061808,0.4628849],"study_design_scores_gemma":[0.00005360402,0.0005148433,0.003767433,0.00004634261,0.0001847069,0.002606459,0.000198488,0.6872556,0.2746032,0.01840979,0.01223325,0.0001262557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04141049,0.0004587976,0.9540727,0.0002445322,0.00009715048,0.00008485689,0.00002738162,0.0007408274,0.002863162],"genre_scores_gemma":[0.5460408,0.0005760295,0.4453661,0.000320929,0.0001429043,0.0001253724,0.0001073595,0.00008154664,0.007239015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001250332,"threshold_uncertainty_score":0.002558291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145380511747083,"score_gpt":0.2453040763549183,"score_spread":0.2338502712374475,"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."}}