{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:38:48Z","timestamp":1775745528920,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>Frame-wise steganalysis is a crucial task in low-bit-rate speech streams that can achieve active defense. However, there is no common theory on how to extract steganalysis features for frame-wise steganalysis. Moreover, existing frame-wise steganalysis methods cannot extract fine-grained steganalysis features. Therefore, in this paper, we propose a frame-wise steganalysis method based on mask-gating attention and bilinear codeword feature interaction mechanisms. First, this paper utilizes the mask-gating attention mechanism to dynamically learn the importance of the codewords. Second, the bilinear codeword feature interaction mechanism is used to capture an informative second-order codeword feature interaction pattern in a fine-grained way. Finally, multiple fully connected layers with a residual structure are utilized to capture higher-order codeword interaction features while preserving lower-order interaction features. The experimental results show that the performance of our method is better than that of the state-of-the-art frame-wise steganalysis method on large steganography datasets. The detection accuracy of our method is 74.46% on 1000K testing samples, whereas the detection accuracy of the state-of-the-art method is 72.32%.<\/jats:p>","DOI":"10.3390\/jcp5030054","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T07:49:58Z","timestamp":1754380198000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Frame-Wise Steganalysis Based on Mask-Gating Attention and Deep Residual Bilinear Interaction Mechanisms for Low-Bit-Rate Speech Streams"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7423-9132","authenticated-orcid":false,"given":"Congcong","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8321-9259","authenticated-orcid":false,"given":"Azizol","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0905-8140","authenticated-orcid":false,"given":"Normalia","family":"Samian","sequence":"additional","affiliation":[{"name":"Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nuur Alifah","family":"Roslan","sequence":"additional","affiliation":[{"name":"Department of Multimedia, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128834","DOI":"10.1016\/j.neucom.2024.128834","article-title":"Invisible and robust watermarking model based on hierarchical residual fusion multi-scale convolution","volume":"614","author":"Zou","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107511","DOI":"10.1016\/j.jfranklin.2025.107511","article-title":"Frequency-domain attention-guided adaptive robust watermarking model","volume":"362","author":"Zhang","year":"2025","journal-title":"J. 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