{"id":"W4411603506","doi":"10.18267/j.aip.271","title":"Enhancing Imperceptibility: Zero-width Character-based Text Steganography for Preserving Message Privacy","year":2025,"lang":"en","type":"article","venue":"Acta Informatica Pragensia","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Character (mathematics); Steganography; Computer science; Zero (linguistics); Zero-knowledge proof; Computer security; Cryptography; Artificial intelligence; Mathematics; Embedding; Linguistics; Philosophy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002628536,0.0004455067,0.0002683766,0.0005533037,0.000237457,0.0004574326,0.0005004818,0.0005237298,0.002184258],"category_scores_gemma":[0.00130796,0.0001298128,0.0003215674,0.0003767603,0.0004907067,0.001136888,0.0005269129,0.0004165199,0.0007584278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002356646,"about_ca_system_score_gemma":0.0002251768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002426658,"about_ca_topic_score_gemma":0.000267811,"domain_scores_codex":[0.9996343,0.00007247021,0.00002298437,0.0000779886,0.0001571772,0.00003508182],"domain_scores_gemma":[0.9992706,0.000259977,0.0001272719,0.0001469108,0.0001671983,0.00002805399],"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":[0.0004391503,0.0001012815,0.0009758236,0.0003780827,0.00003835475,0.0004576093,0.0001835493,0.01638336,0.7453886,0.008444337,0.000604939,0.2266048],"study_design_scores_gemma":[0.00006681011,0.0007945372,0.002557046,0.00006005531,0.00009165432,0.001960952,0.0001048066,0.2206846,0.7583238,0.004917264,0.01038723,0.00005128525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3065071,0.001415033,0.681309,0.0003207271,0.000105513,0.0001454975,0.0001240898,0.0009571674,0.00911576],"genre_scores_gemma":[0.8696474,0.0008714509,0.1234396,0.000103945,0.00005578421,0.00006762996,0.0001467228,0.00008103654,0.005586354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002184258,"threshold_uncertainty_score":0.007307112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124794161671821,"score_gpt":0.2677802144309642,"score_spread":0.255300798263782,"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."}}