# Audio Steganalysis Estimation with the Goertzel Algorithm
## 1 Introduction  
Information security has greatly increased in recent years due to the increased demand for the use of computers and mobile devices. Some fields, such as multimedia encryption systems, steganography, steganalysis, and watermarking [1], have stood out in this field for their attention to information confidentiality. Steganalysis is the science of analyzing and verifying the presence of foreign data in digital files, including audio, images, and videos, through different spatial or frequency techniques. Unlike steganography, which inserts data into digital files, steganalysis detects any changes resulting from the insertion process. When data are inserted into an audio file, the frequency spectrum of the file is modified,which we call this new file stegoaudio. The change in the frequency of the audio file is known as the fingerprint of the audio file [2]. Audio file fingerprints are outliers present in audio files [3]. Therefore, the steganalysis process detects the variation in frequency, or footprint, of the stegoaudio. Footprints can be detected through filtering techniques, frequency analysis, supervised or unsupervised statistical classifications, or statistical analysis [2]. In recent years, various methods have been implemented for stegoimage analysis; however, audio has experienced a slower profession due to the complexity of data processing [4]. Recently, voice communication has become popular among social networks such as WhatsApp, Facebook, TikTok, and YouTube [5]. Communication via voice transmission through smartphones has increased as social networks grow in popularity. These communications require tools that provide greater security and tranquility to end users. Free practical tools exist for hiding secret messages in audio files, such as S-Tools and Hide4 PGP for audio files in.wav format or MP3 Stego for.mp3 files [5]. In many applications, such as dual-tone multifrequency (DTMF) signals, a digital multifrequency (MF) receiver is necessary to recognize frequency components simply and efficiently [6]. A second-order realization of the Goertzel filter is also favored over the direct DFT because it results in reduced computational burden [6]. The Goertzel algorithm is used to compute the DFT spectra. It can be performed from the perspective of the DFT taken over short time sections of the signal (the time window is fixed) or from the perspective of a filtering operation at a given frequency (the frequency is fixed) [7]. Therefore, the Goertzel algorithm is a faster method of pitch detection than a fast Fourier transform and a DFT for a single frequency) [7]. Linear prediction analysis is a tool that has been used in many fields. It has been used in audio signal processing and is one of the most popular and effective methods, especially in the evaluation of basic speech characteristics, spectrum, low-speed transmissions, speech storage, etc. It can accurately evaluate the characteristics of the audio signal and can correctly represent the time domain and frequency domain characteristics of the audio signal with fewer parameters [5]. In this work, we apply the classic Goertzel algorithm to detect possible frequencies that have been altered by the insertion or alteration of the audio vector in conjunction with linear prediction for the detection of possible altered tones. The final vectors are plotted to visualize the alteration zones. The obtained results are measured qualitatively and quantitatively. This work focuses on the following main contributions:  
* By detecting the differences in frequency of the audio signals, the fingerprints of the files are located to compare the audio files. * The method proposed here can detect files of the same type as well as text or image files. * Minimum implementation complexity, unlike methods based on neural networks. The potential frequency range of the alteration of the original audio is identified.