## 2 Related Work  
Steganography and steganalysis in VoIP-based speech streams have emerged as important research areas in information concealing. Because most speech codecs are ACELP-based, present steganography approaches focus on three-parameter domains: pitch delay [12, 13], LPC [14, 15, 31], and FCB [17, 18]. The unpredictability of the pitch period allows for information concealment. [PERSON] et al. [12] suggested a steganography approach for G.723.1 encoded speech that involved changing the closed-loop adaptive codebook encoding procedure. Similarly, [PERSON] et al. [13] proposed a triple-layer steganography solution for G.729 encoded speech that used pitch parameter direction adjustments and dual matrix encoding to improve embedding efficiency. The LPC domain is another potential area for steganographic uses in speech frames. Steganographic approaches in this space usually include splitting LPC codebooks and embedding data during speech quantization process. For example, [PERSON] et al. [31] used a complementary neighbor-vertices technique for codebook partitioning and created the first QIM-based steganographic method for LPC coefficients, which was verified using iLBC and G.723.1 codecs. [PERSON] et al. [14] proposed a QIM-based technique for G.723.1 streams, considering each quantization index set as a point in index space and using a genetic algorithm-based partitioning mechanism to reduce replacement distortion. [PERSON] et al. [15] improved LPC-based steganography in G.723.1 streams by integrating matrix embedding, resulting in higher performance. The pulse position search process involves considerable redundancy, as heuristic depth-first search methods often fail to achieve optimal results. This characteristic makes the Fixed Codebook (FCB) an effective medium for embedding additional information in speech signals [17, 18]. Among the various coding parameters within each speech frame, FCB parameters are the most frequently utilized, particularly in adaptive multi-rate (AMR) speech coding. For instance, in the 12.2 kbps AMR-NB codec, FCB parameters account for 57.38% of the total frame data. As a result, the FCB domain has become a focal point for strategies that introduce auxiliary data within AMR-encoded speech, motivating research into corresponding detection techniques. Early explorations in this direction include the work of [PERSON] and [PERSON] [17], who refined the pulse position search by limiting the selection range to two out of eight possible positions. Building on this, [PERSON] et al. [18] propose an approach that adjusts pulse combinations to accommodate additional signals within the FCB domain, providing a more flexible integration process through the application of an embedding factor. With the wide use of AMR coding in mobile communications, detecting steganography in AMR-encoded speech has attracted growing attention. [PERSON] et al. [19] proposed two features: one based on entropy and another on Markov properties of pulse positions, but they did not handle pulse position swaps well. To address this limitation, [PERSON] et al. [20] estimated the probability of repeated pulse positions to reduce the effect of position changes. However, their method mainly focused on pulses at the same position and lacked the ability to capture more complex patterns. [PERSON] et al. [21] introduced the SCPP method, which includes three types of features: long-term distribution, short-term transitions, and track-to-track correlations. Due to the high feature dimension (2772), they used AdaBoost to reduce it to 498 and avoid overfitting in SVM classifiers. Building on this, [PERSON] et al. [23] proposed an adaptive detection method. They used a Markov matrix and SCPP to extract features, added a convergence metric, and trained an XGBoost model for better accuracy. Although the above steganalysis methods [19; 20; 21; 23] are effective, they share a key limitation: they rely on prior knowledge of the embedding rate in the speech signal.