## 1 Introduction
This directly mitigates the fundamental limitation of relying solely on additive or shallow nonlinear transformations used in existing steganalysis models. By integrating these mechanisms within a deep residual network framework, our method is able to capture both lower-order and higher-order codeword feature interactions in a unified and theoretically grounded manner. This design not only addresses the architectural limitations of existing methods [33; 34] but also improves the extraction of discriminative steganalysis features at the frame level. Therefore, this paper proposes a frame-wise steganalysis method that combines mask-gating attention [36] and bilinear codeword feature interaction [37] mechanisms. The mask-gating attention module learns to dynamically assess the importance of individual codewords within a frame, while the bilinear interaction module captures informative second-order interaction patterns among codewords. Finally, multiple fully connected layers with residual connections are employed to model higher-order interactions while effectively preserving lower-order information. Existing frame-wise steganalysis methods [33; 34] neglect the correlations between codewords at the vector level. Specifically, they first use the embedding layer to map all the codewords in the frame into codeword vectors, then combine those codeword vectors into a large vector, which restricts subsequent operations to the bit-wise level. These methods only learn higher-order features and are not compatible with lower-orderfeatures. Moreover, the parameters of these methods are difficult to optimize, and the models are prone to convergence issues [35]. Specifically, because all the operations of existing steganalysis methods are performed at the bit-wise level, the higher-order features are obtained through nonlinear functions. For frame-wise detection [33, 34] in low-bit-rate speech streams, the main challenge is how to extract steganalysis features from individual speech frames. Therefore, in this paper, we propose a frame-wise steganalysis method based on mask-gating attention [36] and bilinear codeword feature interaction [37] mechanisms. First, we utilize the mask-gating attention mechanism to dynamically learn the importance of the codewords. Second, bilinear feature interaction is used to capture informative second-order codeword interaction patterns. Finally, multiple fully connected layers with residual structures are utilized to capture higher-order interaction features while preserving lower-order interaction features. The contributions of this paper are as follows:  
* By introducing a mask-gating attention mechanism, the approach overcomes the uniform weighting limitation in existing frame-wise steganalysis methods, enabling dynamic emphasis on informative codewords and effective extraction of global contextual information. * Through the integration of bilinear feature interaction layers and a deep residual structure, the proposed method addresses the neglect of vector-level codeword correlations in existing approaches, capturing both lower- and higher-order interaction patterns for improved frame-wise detection in low-bit-rate speech streams.