{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T17:10:29Z","timestamp":1788801029660,"version":"build-2803163510"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2268203"],"award-info":[{"award-number":["U2268203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["U2268203"],"award-info":[{"award-number":["U2268203"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The proliferation of bullet comment systems on video-sharing platforms has created novel opportunities for covert communication. Recent research has demonstrated multiple bullet comment-based steganographic paradigms: time modulation, time attribute shifting, live broadcast game-based channels, and generative stegotext via frame comments. This paper presents Multi-Dimensional Behavioral Signature Analysis (MDBSA), a unified detection framework integrating seven behavioral dimensions to systematically characterize anomalies from four documented bullet comment steganographic paradigms. We construct a synthetic benchmark spanning six video categories with controlled steganographic injection; extract features capturing temporal, spatial, content, and structural patterns; and evaluate it using GroupKFold cross-validation to prevent data leakage from overlapping sliding windows. On this synthetic benchmark, MDBSA features combined with Gradient Boosting achieve 96.9% accuracy (F1 = 96.8%, AUC = 0.996), compared with 65.6% for logistic regression on the same features. Per-paradigm detection rates on the benchmark range from 98.4% to 100.0%.<\/jats:p>","DOI":"10.3390\/e28090999","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T17:06:58Z","timestamp":1788800818000},"page":"999","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Dimensional Behavioral Signature Analysis for Video Bullet Comment Steganography Detection"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4551-4246","authenticated-orcid":false,"given":"Yitong","family":"Liu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Hongwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, Network and Information Technology Center, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Katzenbeisser, S., and Petitcolas, F.A.P. 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