## 1 Introduction
Transparency is the imperceptibility of changes in the carrier after embedding, ensuring that changes are undetected via the human auditory or visual system. In contrast, embedding capacity measures the amount of secret information hidden inside the carrier. By optimizing these two parameters, the adaptive technique attempted to increase overall steganographic performance. In recent years, various research [19, 20, 21, 22, 23] has concentrated on detecting steganographic techniques employed in AMR-based voice streams, with remarkable results. These steganalysis techniques generally employ a \"feature plus classifier\" structure, with support vector machines (SVMs) [19, 20, 21] serving as the primary classifier. The research focuses mostly on the creation of handmade characteristics that are customized to the specific aspects of AMR speech. For example, [PERSON] et al. [19] suggested two statistical feature types for assessing pulse placements: Markov transition probabilities, which capture transition dynamics, and joint and conditional entropies, which reflect the probability distribution of pulses at certain places. However, these attributes ignore potential pulse position changes throughout the AMR encoding process, limiting their ability to adequately define AMR speech. [PERSON] et al. [20] overcame this constraint by introducing features with similar pulse position probability. Despite improvements, these features failed to account for the distributions of two-track pulses at different places, limiting their usefulness. To address these issues, [PERSON] et al. [21] created a broader feature set that included long-term distribution features, short-term invariants, and track-to-track correlations. This set outperformed prior designs. However, its high dimensionality (2772 features) required a dimensionality reduction, which was accomplished using adaptive boosting (AdaBoost), lowering the feature set to 498 dimensions while maintaining effectiveness. [PERSON] et al. [22] present a steganalysis approach with a multiple-classifier combination model at its foundation. There are three phases to the scheme. Initially, steganalysis features are fed into two different classifier sets, yielding the first and second types of prediction outputs. The second set of predictions is then handled as a unique form of detection feature and fed into an additional classifier set to produce the third type of prediction results. Finally, the three types of predictions are combined to generate the final detection result. [PERSON] et al. [23] developed an adaptive steganalysis model for AMR-encoded speech. The method starts by creating a Markov transition matrix from the original speech signal to extract the statistical characteristics of pulse pairs (SCPPs) as the key feature. To capture global patterns, a convergence feature is used. These features are then combined and passed via an extreme gradient boosting (XGBoost) classifier. Model training optimizes the system for effective detection. The experimental findings showed that this method works effectively in detecting steganography in AMR-based speech streams. Although previous studies have made significant progress in FCB-based steganalysis [18; 20; 21; 22; 23], they generally assume that the embedding rate--the proportion of hidden data in a speech sample--is known in advance. This assumption is often unrealistic in practice, emphasizing the importance of developing detection methods that can operate without prior knowledge of the embedding rate. To address this, [PERSON] et al. [24] proposed a method based on Dempster-Shafer Theory (DST), which combines the outputs of multiple classifiers trained at different embedding rates to produce a final decision. However, this approach faces several issues: it performs poorly at low embedding rates due to data mismatch, struggles with high-dimensional features when using SVM classifiers, and lacks robustness to small deviations in the actual embedding rate. To improve adaptability, [PERSON] et al.