Audio Steganography Method Research Articles

A Method of Cover Audio Selection for Embedding Based on Various Criteria

The main goal of an audio steganography method is to improve the capacity, im-perceptibility, and robustness. Several methods of audio steganography have been proposed to enhance its capabilities. To further optimize the efficiency of the audio steganography method, this paper proposes to select the appropriate audio cover for concealing the message. The selection of an appropriate cover can significantly improve the quality of the output and facilitate the development of innovative audio steganography techniques. This paper proposed a new audio cover selection method, which can further enhance the characteristics of the resulting output. This method ranked each cover according to the qualities that need to be boosted using various measurements. These measurements are the Maximum Available Space (MAS), Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER) for capacity, imperceptibility, and robustness respectively. Based on the experiment conducted using five cover audios stored in the self-created database, each audio is ranked differently based on the measurement used to determine its characteristic level. In conclusion, the proposed cover selection method can be used to select the most proper cover, hence improving the characteristics of audio steganography.

ANALYSIS OF COMMON AUDIO STEGANOGRAPHY METHODS

In the face of modern challenges, the protection of vital interests of a person and a citizen is fundamental to the issue of national security. In this context, the ability to freely transfer confidential information is very important. One of the possible solutions is the use of steganographic methods. Digital audio steganography has advantages over other types of steganography. This makes it important to analyze the most common audio steganography methods.

Cover Enhancement Method for Audio Steganography Based on Universal Adversarial Perturbations with Sample Diversification

Cover Enhancement Method for Audio Steganography Based on Universal Adversarial Perturbations with Sample Diversification

Novel approach for file encryption

A novel approach to file Encryption is concerned with protecting files being shared among users. The idea revolves around storing files securely so that If someday database is hacked, then data doesn’t leaked. To ensure files are protected they are split in 3:1 ration and encrypted using various algorithms like Advance Encryption System (AES) and Data Encryption Standard (DES). Audio steganography method has also been used to improve protection standard.

A New Method to Estimate Peak Signal to Noise Ratio for Least Significant Bit Modification Audio Steganography

Audio steganography is implemented based on three main features: capacity, robustness, and imperceptibility, but simultaneously implementing them is still a challenge. Embedding data at the Least Significant Bit (LSB) of the audio sample is one of the most implemented audio steganography methods because the method will give high capacity and imperceptibility. However, LSB has the lowest robustness among all common methods in audio steganography. To cater to this problem, researchers increased the depth of the embedding level from fourth to sixth and eighth LSB level to improve its robustness feature. However, consequently, the imperceptibility feature, which is commonly measured by Peak Signal to Noise Ratio (PSNR), is reduced due to the trade-off between imperceptibility and robustness. Currently, the lack of study on the estimation of the PSNR for audio steganography has caused the early assessment of the imperceptibility-robustness trade-off difficult. Therefore, a method to estimate PSNR, known as PSNR Estimator (PE), is introduced to enable early evaluation of imperceptibility feature for each stego-file produced by the audio steganography, which is important for the utilisation of embedding. The proposed PE estimates the PSNR based on the pattern collected from the embedment at different levels. From the evaluation, the proposed method has 99.9% of accuracy in estimating PSNR values at different levels. In comparison with the Mazdak Method, the proposed method performs better in all situations. In conclusion, the proposed PE can be used as a reference for embedding and further reducing the calculation complexity in finding the feasible value to minimise the trade-off between robustness and imperceptibility.

An Audio Steganography Based on Run Length Encoding and Integer Wavelet Transform

This paper proposes an audio steganography method based on run length encoding and integer wavelet transform which can be used to hide secret message in digital audio. The major contribution of the proposed scheme is to propose an audio steganography with high capacity, where the secret information is compressed by run length encoding. In the applicable scenario, the main purpose is to hide as more information as possible in the cover audio files. First, the secret information is chaotic scrambling, then the result of scrambling is run length encoded, and finally, the secret information is embedded into integer wavelet coefficients. The experimental results and comparison with existing technique show that by utilizing the lossless compression of run length encoding and anti-attack of wavelet domain, the proposed method has improved the capacity, good audio quality, and can achieve blind extraction while maintaining imperceptibility and strong robustness.

Learning to Generate Steganographic Cover for Audio Steganography Using GAN

Audio steganography aims to exploit the human auditory redundancy to embed the secret message into cover audio, without raising suspicion when hearing it. However, recent studies have shown that the existing audio steganography can be easily exposed with the deep learning based steganalyzers by extracting high-dimensional features of stego audio for classification. The existing GAN-based steganography approaches mainly studied in images cover, less work is conducted on audio cover. In addition, though a few GAN-based audio steganography methods have been proposed, they still have room for improvements in perceptual quality and undetectability. In this work, we propose an audio steganography framework that could automatically learn to generate superior steganographic cover audio for message embedding. Specifically, the training framework of the proposed framework consists of three components, namely, generator, discriminator and trained deep learning based steganalyzer. Then the traditional message embedding algorithm LSBM, is employed to embed the secret message into the steganographic cover audio to obtain stego audio, which is delivered to the trained steganalyzer for misclassifying as cover audio. Once the adversarial training is completed among these three parties, one can obtain a well-trained generator, which could generate steganographic cover audio for subsequent message embedding. In the practice of our proposed method, the stego audio is produced by embedding the secret message into the steganographic cover audio using a traditional steganography method. Experimental results demonstrate that our proposed audio steganography can yield steganographic cover audio that preserves a quite high perception quality for message embedding. We have compared the detection accuracies with the existing audio steganography schemes as presented in our experiment, the proposed method exhibits lower detection accuracies against the state-of-the-art deep learning based steganalyzers, under various embedding rates. Codes are publicly available at https://github.com/Chenlang2018/Audio-Steganography-using-GAN.

SmartSteganogaphy: Light-weight generative audio steganography model for smart embedding application

Massive data is transferred in the Internet of Thing (IoT) every single second. Protecting the security of these data is a crucial task. Steganography is a collection of techniques for concealing the existence of information by embedding it within irrelevant carriers, which could protect security and privacy of the data. Distinct from the cryptography, steganography put emphasis on hiding the existence of the secret. However, the majority of research mainly work on complex algorithm. In this paper, we proposed an audio steganography algorithm which automatically generated from adversarial training. The embedding model is light-weight which could use as machine learning tools in smart device. Besides, the existing audio steganography methods mainly depend on human handcraft, while the proposed method could obtain from meachine learning. The embedding model consists of three neural networks: encoder which embeds the secret message in the carrier, decoder which extracts the message, and discriminator which determines the carriers containing secret messages. The system is trained with different training settings on two datasets. Competed the majority of audio steganographic schemes, the proposed scheme could produce high fidelity steganographic audio. Besides, the extensive experiments demonstrate the robustness and security of our algorithm.

A novel quantum steganography-Steganalysis system for audio signals

As a substitute for classical solutions, quantum information hiding techniques have become an essential issue in the field of quantum communications by utilizing the inherent features of quantum mechanics and creating more secured communications for the more reliable exchange of digital media within the context of quantum communications networks. Quantum steganography has been considered as one of these approaches in recent years, but the importance of investigating and discovering these hidden communications within the context of quantum communication networks that require the use of quantum steganalysis methods has not been addressed so far. Therefore, in this paper, a novel quantum steganography-steganalysis system for digital audio signals is proposed, which can accurately detect audio steganography methods in the context of quantum communication networks. The proposed model consists of two separate sections: steganography and steganalysis; in the steganography part, to minimize the impacts of the embedding process and increasing the Signal to Noise Ratio (SNR), the embedding operation is carried out within the Least Significant Fractional Qubit (LSFQ) of the amplitude information of the audio signal samples. Then, a universal steganalyzer in the steganalysis part distinguishes the stego audio signals using the extracted statistical features from the audio signals. The universal steganalyzer consists of a mean feature extraction module to extract features from the audio signal frames and the quantum circuits for implementing the K-Nearest Neighbor (KNN) algorithm and the Hamming distance criterion. The simulation-based quantum circuits of the proposed system tested and evaluated using different audio files. Over 80% accuracy in detecting stego audio signals indicates high accuracy and efficiency of the proposed scheme and its applicability in quantum communication networks. Along with the higher efficiency and security of quantum steganography methods when compared with the classical one, the results show that the proposed quantum steganography-steganalysis scheme is also capable of competing with classical methods in terms of accurately detecting steganography methods.

High capacity reversible data hiding in MP3 based on Huffman table transformation.

In practice, most audio files such as MP3 and AAC are stored and transmitted in the form of compressed files, which can serve as the cover in audio steganography. Currently, the prevailing audio steganography methods are not ideal because of the drawbacks. Some are characterized by low capacity while others are irreversible. In this paper, we propose a method to embed secret messages in MP3 encoding. Our strategy is to hide the information by Huffman table transformation. We extract secret information by analyzing side information. Experimental results show that our method can greatly improve the steganographic capacity with low distortion and high security. Meanwhile, it featured with higher decoding rate and reversible over some state-of-the-art methods.

An Adaptive Audio Steganography for Covert Wireless Communication

In recent years, the wide applications of the wireless sensor networks have achieved great success. However, the security is a critical issue in many scenarios ranging from covert military operations to the organization of the social unrest. Because the traditional encrypting methods are easy to arouse suspicion, an adaptive audio steganography method is proposed. The method is based on interval and variable low bit coding, which can be applied to covert wireless communication. The interval for embedding secret messages into the audio file and the threshold in variable low bit coding are used for selecting the embedding location and embedding bits adaptively; thus the embedding capacity and the embedding rate are variable. Experimental results demonstrate that the proposed method has better performance in embedding rate and invisibility than other audio steganography methods.

A DCT–SVD-Based Speech Steganography in Voiced Frames

A novel speech steganography method is presented in this paper by cascading discrete cosine transform and singular value decomposition. The significance of the proposed method is that it utilizes the voiced part of the speech signal, making it highly imperceptible and robust. Imperceptibility of the proposed method is quantified by both signal-to-noise ratio and subjective listening tests. Our experimental results of bit error rate demonstrate that the proposed audio steganography method is robust against MP3 compression, AWGN, re-sampling and re-quantization attack with high data payload. The undetectability of proposed method is verified using a powerful steganalysis tool, and the results are compared with a well-known steganographic technique.

Blind Key Based Attack Resistant Audio Steganography Using Cocktail Party Effect

Steganography is a popular technique of digital data security. Among all digital steganography methods, audio steganography is very delicate as human auditory system is highly sensitive to noise; hence small modification in audio can make significant audible impact. In this paper, a key based blind audio steganography method has been proposed which is built on discrete wavelet transform (DWT) as well as discrete cosine transform (DCT) and adheres to Kerckhoff’s principle. Here image has been used as secret message which is preprocessed using Arnold’s Transform. To make the system more robust and undetectable, a well-known problem of audio analysis has been explored here, known as Cocktail Party Problem, for wrapping stego audio. The robustness of the proposed method has been tested against Steganalysis attacks like noise addition, random cropping, resampling, requantization, pitch shifting, and mp3 compression. The quality of resultant stego audio and retrieved secret image has been measured by various metrics, namely, “peak signal-to-noise ratio”; “correlation coefficient”; “perceptual evaluation of audio quality”; “bit error rate”; and “structural similarity index.” The embedding capacity has also been evaluated and, as seen from the comparison result, the proposed method has outperformed other existing DCT-DWT based technique.

Audio steganalysis based on reversed psychoacoustic model of human hearing

During the last decade, audio information hiding has attracted lots of attention due to its ability to provide a covert communication channel. On the other hand, various audio steganalysis schemes have been developed to detect the presence of any secret messages. Basically, audio steganography methods attempt to hide their messages in areas of time or frequency domains where human auditory system (HAS) does not perceive. Considering this fact, we propose a reliable audio steganalysis system based on the reversed Mel-frequency cepstral coefficients (R-MFCC) which aims to provide a model with maximum deviation from HAS model. Genetic algorithm is deployed to optimize dimension of the R-MFCC-based features. This will both speed up feature extraction and reduce the complexity of classification. The final decision is made by a trained support vector machine (SVM) to detect suspicious audio files. The proposed method achieves detection rates of 97.8% and 94.4% in the targeted (Steghide@1.563%) and universal scenarios. These results are respectively 17.3% and 20.8% higher than previous D2-MFCC based method.

A Novel Approach to Secure Communication Using Audio Steganography

The digital information revolution has brought important changes in our society and life. Nowadays, large amount of data is transmitted over the network and if the data that is being transmitted is important, one should use secure technique like steganography to transmit it. Steganography is a method of hiding a secret message in a cover media such as text, image, audio etc. in a way that hides the existence of the secret data. This paper introduces new method for audio steganography. The proposed method works on the basis of low bit blind encoding scheme which is used to embed secret data into non-silent samples of wav audio file. Robustness and performance of the proposed scheme is investigated by performing experiments on different audio signals.

Unified Evaluation System for Audio Steganography Methods

The interest in the digital steganography grows together with the rise of amounts of multimedia data exchanged over the Internet. This growth provides steganography with new possibilities and opportunities for data hiding. The information hiding in audio streams is considered especially promising due to audio data popularity and possibilities to hide data within audio signals. There are many data hiding methods. The steganography is used to process audio streams, and the new methods are constantly emerging. These methods have different applications and targets, features and properties. This paper presents the Unified Evaluation System (UES) for evaluation, comparison and classification of audio steganography methods proposed by the authors. UES is a set of calculation principles, which are especially combined for audio steganographic methods. The system’s outcomes provide conclusions in order to determine advantages/disadvantages of the methods and to define the audio steganography methods as the most suitable for each particular case.

Adaptive Digital Audio Steganography Based on Integer Wavelet Transform

In this paper a novel method for digital audio steganography is presented where encrypted covert data is embedded into the coefficients of the host audio (cover signal) in the integer wavelet domain. The hearing threshold is calculated in the integer domain and this threshold is employed as the embedding threshold. The inverse integer wavelet transform is applied to the modified coefficients to form a new audio sequence (stego signal). The characteristics of this method are large payload, high audio quality and full recovery.