## 2 Related Works  
For the detection of QIM-based steganography [17, 18, 19, 20, 21], existing steganalysis methods [21, 23, 24, 25, 26, 27, 28, 29, 30, 31] can be divided into two types. The first type is segment-wise detection methods [23, 24, 25, 26, 27, 28, 29, 30, 31], which are aimed at detecting whether a segment of a speech stream contains secret information. Segment-wise steganalysis methods mainly involve two stages. In the first stage, many steganalysis methods [24, 25, 26, 27, 28, 29, 32] adopt the pattern of handcrafted features plus a classifier. Many researchers [24, 25, 26, 32] utilize the statistical characteristics of the codebook correlation feature to extract steganalysis features. Common statistical characteristics used include the Markov transition matrix, entropy, and conditional probability. These features are employed to analyze the presence of hidden information in speech streams. Specifically, in QIM steganography [23, 24, 25, 26], secret information is embedded into low-bit-rate speech streams by modifying the LPC (Linear Predictive Coding) indices. [PERSON] et al. [26] observed this change in LPC indices and proposed a method to quantify the correlated characteristics of these indices. To quantify the correlation, [PERSON] et al. used first-order Markov transition probabilities. These probabilities are employed to construct a categorical feature vector for each LPC index. This vector captures the relevant properties and variations in the LPC indices, allowing for further analysis and manipulation of speech streams for steganographic purposes. Furthermore, Bayesian network (BN)-based techniques have been proposed to detect hidden information. One example is the steganography-sensitive Codeword Bayesian Network (CBN) introduced in [33], which leverages the correlation changes of codeword spatiotemporal transitions. The CBN model takes into account the correlation patterns and transitions of codewords in order to detect steganographic content. Bayesian inference is then employed for classification tasks, allowing for the accurate identification of hidden information. During the second stage of research development [27, 28, 29, 30, 31], many deep learning-based approaches have emerged, motivated by the success of deep learning in fields such as computer vision and natural language processing. Researchers have explored modelssuch as Recurrent Neural Networks (RNNs) [21, 27, 31], convolutional neural networks (CNNs) [21, 31], and attention mechanisms [28, 29] to improve steganalysis in VoIP streams. For example, [PERSON] et al. [27] proposed an RNN-based method to capture temporal dependencies in quantization index sequences, marking the first deep learning approach for QIM steganography detection in VoIP. [PERSON] et al. [29] introduced a model leveraging attention mechanisms to extract correlation features for steganalysis. They further enhanced this work [28] by combining an attention module with a convolutional network to model the semantic hierarchical structure of speech, leading to improved detection performance. [PERSON] et al. [21] presented the Steganalysis Feature Fusion Network (SFFN), a steganalysis framework that integrates Long Short-Term Memory (LSTM) and a CNN and is designed to detect multiple parallel steganography techniques and improve detection generality. [PERSON] et al. [30] developed the key feature extraction and fusion network to enable fast and efficient detection. Additionally, [PERSON] et al. [31] proposed a steganalysis approach based on code element (CE) embedding, utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) and a CNN with attention mechanisms. Beyond these, another important category is frame-wise detection methods [33, 34], which aim to determine whether each frame contains covert information. Compared to segment-wise detection, frame-wise detection is essential for achieving low-latency and imperceptible active steganography defense. This paper, therefore, focuses on frame-wise detection. For this task, [PERSON] et al.