# Audio Steganalysis Estimation with the Goertzel Algorithm
## 2 Materials and Methods  
Few steganalysis methods exist for audio files; as a consequence, audio steganalysis is a necessary tool for detecting possible intrusions [8]. The popularity of streaming audio files in either.WAV or.MP3 format in digital media can result in vulnerabilities [4]. This requires generating techniques and mechanisms applicable to intervention analysis.  
Some works related to the topic of audio file analysis are described in this section. Recent developments have been made in analyzing audio for hidden message detection. These developments include the following documented works.  
In 2003, [PERSON] et al. proposed a universal audio steganalysis technique based on the quality metrics of an audio file. This technique is efficient for both the watermarking and steganographic methods. The idea in [8] is based on statistical evidence that the distortion measures calculated between the original audio file and the file with embedded data should have statistically distinguishable distributions for the information-bearing signals and the stegosignals [8]. Based on these quality metrics, the test files are examined to determine whether the audio is a stegoaudio.  
In 2004, [PERSON] et al. proposed a method based on a block decoder of an MP3 audio file, and subsequently applied a method based on the psychoacoustic model to identify characteristics [9].  
In 2005, [PERSON] et al. proposed a speech record stegoanalysis algorithm based on LSB embedding in conjunction with the Hide4 PGP algorithm for files with sampling rates of 44 and 100 to 16 bits per sample [10].  
In more recent years (2015), [PERSON] et al. [11] proposed a stegoanalyzer incorporating interframe Markov analysis for audio files modified through MP3 Stego.  
In 2018, [PERSON] et al. applied linear prediction in conjunction with a Support Vector Machine (SVD) with a Gaussian radial basis function (RBF) kernel. This was used as a classifier and coupled with a k-fold cross-validation method to identify the best parameters and thus improve the anomalous feature detection accuracy [5].  
In 2020, [PERSON] et al. proposed a steganalysis method based on quantum analysis. His method consists of two sections [1]. The first section is the steganography of the audio file based on the classic Last Bit Significant steganography algorithm but in terms of quantum computing, consisting of steganography in the insertion of data into the least significant fractional qubit (LSFQ) within the amplitude of the audio signal samples. In the second section, steganalysis is performed in a feature extraction module from the audio signal frames and quantum circuits, implementing the KNearest Neighbor (KNN) algorithm and the Hamming distance criterion [1].  
In 2022, [PERSON] et al. proposed using the spectrogram as an input feature to extract information. DeepResNet is then applied to learn the representations of distinctive features, and the multiscale spectrograms enrich the diversity of input features [12].  
A common analysis tool for frequency analysis in detecting signal frequency changes is the Fast Fourier Transform (FFT); however, its high computational cost complicates its application in architectures with limited processing capacity.  
Hence, in this research proposal, we present a stegoanalysis technique for audio files based on an adaptation of the frequency analysis. The analysis is performed in conjunction with a statistical analysis and Audio Quality Metrics (AQM) [8] between the audio signals and possible stegoaudio.