## 1 Introduction
The second category of steganalysis methods leverages deep learning techniques [21, 27, 28, 29, 30, 31], including Recurrent Neural Networks (RNNs) [21, 27, 31], convolutional neural networks (CNNs) [21, 31], and attention mechanisms [28, 29], to automatically extract discriminative features from VoIP streams. These approaches, which benefit from the powerful representation capabilities of neural networks, have demonstrated promising performance in detecting various steganographic schemes. However, the steganalysis methods described above are intended to detect whether a segment of the speech stream contains secret information. In comparison to segment-wise detection [21, 23, 24, 25, 26, 27, 28, 29, 30, 31], frame-wise detection [33, 34] is a key prerequisite for achieving unperceived active steganography defense. The value of some codewords in the frame can be slightly adjusted given the revised embedding position. The hidden channel is destroyed, preventing the receiver from fully extracting the secret information. As a result, this paper focuses on frame-wise detection. Frame-wise detection in low-bit-rate speech streams [33, 34] presents specific challenges for steganalysis feature extraction. Unlike segment-wise detection [21, 23, 24, 25, 26, 27, 28, 29, 30, 31], frame wise detection [33; 34], which leverages temporal correlations and spatial information across longer contexts, treats each frame in isolation. This design choice restricts the ability to utilize temporal dependencies and eliminates access to spatial relationships that exist across the entire speech stream. As a result, identifying covert modifications becomes significantly more difficult. To address frame-wise detection, [PERSON] et al. [33] proposed a method based on multi-dimensional codeword correlation modeling that captures four dimensions: global-to-local, local-to-global, forward, and backward. [PERSON] et al. [34] subsequently introduced a dual-domain representation approach. Despite their effectiveness in certain scenarios, these methods exhibit fundamental limitations that restrict their capacity to fully capture frame-level steganalysis features. First, existing frame-wise steganalysis methods primarily operate at the bit-wise level and overlook vector-level codeword interactions. Typically, they transform discrete codeword indices into embedding vectors, which are then concatenated into a single large vector. Subsequent processing relies on bit-wise operations without explicitly modeling the interactions among the embedded codewords. This design choice hinders the model's ability to learn rich, meaningful dependencies within a frame. Second, these methods predominantly focus on extracting higher-order features through stacked nonlinear transformations while lacking explicit mechanisms to preserve and exploit lower-order interactions. This limitation can result in models with complex, difficult-to-optimize parameter spaces and can lead to convergence issues during training [35]. Our approach introduces two mechanisms specifically designed to overcome these limitations, representing novel applications in the field of speech steganalysis. The first mechanism is the mask-gating attention mechanism [36], which is designed to learn adaptive importance weights for embedded codewords within a frame. This mechanism enables the model to dynamically emphasize the most informative codewords while simultaneously capturing global contextual information across the entire frame. Theoretically, it addresses the limitation of uniform or static weighting schemes employed by previous methods, offering a principled means to allocate modeling capacity where it is most impactful. The second mechanism is the bilinear feature interaction module, which explicitly captures second-order multiplicative interactions between codeword embeddings. According to established feature interaction theory [37], bilinear operations enable the efficient and expressive learning of pairwise feature combinations in a parameter-efficient manner.