Cover Audio Research Articles

A robust audio steganography technique based on image encryption using different chaotic maps

The development of innovative methods for concealing critical data in multimedia files has exploded in information security in recent years. Cryptography and steganography cannot be used alone to protect data; rather, they can be combined and used in a single system. Audio steganography is among the most important information security techniques. It involves the concealment of information within audio signals to achieve covert communication. This paper introduces a comprehensive technique that integrates chaos Henon, Baker, and Arnold maps for image encryption with audio steganography to create a robust and secure audio steganography technique. First, the target image is encrypted using chaotic maps. Then, it is embeded within the high frequencies of the cover audio signal based on the Inverse Short Time Fourier Transform (ISTFT) to be transmitted to the destination through the channel. By integrating both encryption and concealment techniques, the cover audio signal quality can be preserved. Moreover, the hidden image security and robustness are improved, making the technique resistant to many types of attacks. The simulation results confirm that the suggested technique is robust in the presence of attacks. It achieves a distinct perceptual quality with an appreciated peak signal-to-noise ratio (PSNR) of 91.2 dB and a Mean Square Error (MSE) of 7.5 × 10–10. The randomness of the resulting encrypted image has successfully passed the National Institute of Standards and Technology (NIST) statistical test suite.

High-capacity steganography through audio fusion and fission

<span>Information security is required for two reasons, either to conceal the information completely or to prevent the misuse of the information by adding watermarks or metadata. Audio steganography uses audio signals to hide secret information. In the proposed audio steganography technique, cover audio files and secret audio files are transformed from time domain to wavelet domain using discrete wavelet transform, the secret audio file is transformed in two levels, leading to secure and high-capacity data hiding. 1% of the 2-level compressed secret is fused to 99% of the 1-level compressed cover. “Peak signal to noise ratio and mean squared error, Pearson’s correlation coefficient, spearman’s correlation coefficient, perceptual evaluation of speech quality and short-time objective intelligibility” are considered to assess the similarity of cover audio and stego audio and similarity of secret audio embedded, and secret audio retrieved. Results show that the stego audio signal is perceptually indistinguishable from the cover audio signal. The approach also passed the robustness test.</span>

Information Security for Audio Steganography Using a Phase Coding Method

The art and science of steganography are dedicated to concealing the presence of a secret message from a third party, such that only the sender and recipient are aware of its content. Various types of media can be used to conceal these communications. When information is hidden in an audio signal, this is referred to as audio steganography. In this paper, two distinct steganography techniques are combined with a multi-level steganography approach: the initial message is embedded in an audio cover at the initial stage, employing, a modified LSB technique, additionally, the second message is embedded in the output from the first level, using a phase coding approach at the second level. A stego audio file is the second level's output containing two audio covers with secret messages. The message is split in multiple ways, with varying proportions between the two levels, in order to investigate how the message's size affects the two procedures used here as well as the levels. The PSNR, MSE, and histogram metrics are used to compare the original and stego audio, in order to assess the effectiveness of the suggested approach. The optimum outcome is achieved when the message is divided in the ratio (1:1). The worst outcome is achieved when the message is divided in the ratio (3:1)

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.

Image Hiding in an Audio File Using Chaotic Method via Genetic Algorithm

Information security is one of the most important parts of information technology and communication in the modern world. Information security has gained a lot of attention recently due to the need to transmit information through global communication channels and through the Internet. In this research, the genetic algorithm is used to develop the chaotic maps that are randomly generated by the chaotic system to find the best map by which the locations of image data are inferred to be hidden in the audio. An average image size of 64×64 pixels is used to be hidden in an audio recording of 7 seconds duration at average resolution specifications. The results show that one of the chaotic maps created at the 739 generations is the best in determining image byte hiding locations in the high-frequency band of the audio. The quality measures show the cover audio that includes the hidden image is not much different from the original input one. This confirms the validity of the proposed method and the good selection of hiding locations in accordance with information security concepts.

A Coverless Audio Steganography Based on Generative Adversarial Networks

Traditional audio steganography by cover modification causes changes to the cover features during the embedding of a secret, which is easy to detect with emerging neural-network steganalysis tools. To address the problem, this paper proposes a coverless audio-steganography model to conceal a secret audio. In this method, the stego-audio is directly synthesized by our model, which is based on the WaveGAN framework. An extractor is meticulously designed to reconstruct the secret audio, and it contains resolution blocks to learn the different resolution features. The method does not perform any modification to an existing or generated cover, and as far as we know, this is the first directly generated stego-audio. The experimental results also show that it is difficult for the current steganalysis methods to detect the existence of a secret in the stego-audio generated by our method because there is no cover audio. The MOS metric indicates that the generated stego-audio has high audio quality. The steganography capacity can be measured from two perspectives, one is that it can reach 50% of the stego-audio from the simple size perspective, the other is that 22–37 bits can be hidden in a two-second stego-audio from the semantic. In addition, we prove using spectrum diagrams in different forms that the extractor can reconstruct the secret audio successfully on hearing, which guarantees complete semantic transmission. Finally, the experiment of noise impacts on the stego-audio transmission shows that the extractor can still completely reconstruct the semantics of the secret audios, which indicates that the proposed method has good robustness.

A Robust Audio Steganography Method Using Partial Knight Tour for Concealing Messages of Text and Image

The rapid growth in transfer of data over the Internet realized the need to secured data transmission. Moreover, there is a need to send message that contains a text and image. The steganography is the most promising technique for facilitating the secure communication without affecting the data quality. In this paper, we propose a robust audio steganography approach that hides secret messages of text and image in an audio WAV file using partial knight tour positions in 4×8 square blocks. The cover audio is divided into 4×8 (or 32) audio sample blocks. The secret data are converted to bits and selected 4×8 audio bytes then can hide ASCII codes of 4 characters. The hiding of secret data block is based on a random selected starting audio byte, and on a random selected tour of 32 predefined tours. The algorithm changes the least significant bit (LSB) of the audio bytes based, as mentioned, on randomly selected tours and randomly selected starting audio byte. The resulted stego file can be, then, transmitted over the communication channel such as Internet to the receiver side. After receiving, the extraction process is performed to extract the secret message. The proposed algorithm is implemented us[ing C# programming languages. The PSNR and MSE parameters are used to evaluate the efficiency of the proposed algorithm and they will be checked after implementation. The proposed technique provides a high quality of Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR).

Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm

Existing embedding techniques depend on cover audio selected by users. Unknowingly, users may make a poor cover audio selection that is not optimised in its capacity or imperceptibility features, which could reduce the effectiveness of any embedding technique. As a trade-off exists between capacity and imperceptibility, producing a method focused on optimising both features is crucial. One of the search methods commonly used to find solutions for the trade-off problem in various fields is the Multi-Objective Evolutionary Algorithm (MOEA). Therefore, this research proposed a new method for optimising cover audio selection for audio steganography using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which falls under the MOEA Pareto dominance paradigm. The proposed method provided suggestions for cover audio to users based on imperceptibility and capacity features. The sample difference calculation was initially formulated to determine the maximum capacity for each cover audio defined in the cover audio database. Next, NSGA-II was implemented to determine the optimised solutions based on the parameters provided by each chromosome. The experimental results demonstrated the effectiveness of the proposed method as it managed to dominate the solutions from the previous method selected based on one criterion only. In addition, the proposed method considered that the trade-off managed to select the solution as the highest priority compared to the previous method, which put the same solution as low as 71 in the priority ranking. In conclusion, the method optimised the cover audio selected, thus, improving the effectiveness of the audio steganography used. It can be a response to help people whose computers and mobile devices continue to be unfamiliar with audio steganography in an age where information security is crucial.

Identifying optimal message embedding location in audio steganography using generative adversarial networks

Audio steganography (AS) uses the auditory redundancy of the human ear to conceal the hidden message inside the audio track. In recent studies, deep learning-based steganalysis has swiftly revealed AS by extracting high-dimensional stego acoustic features for categorization. There is still an opportunity for improvement in the current audio steganography required for managing communication confidentiality, access control and data protection. The main objective of this research is to improving the data protection by identifying the data embedding location in the audio. Generative Adversarial Network-based Audio Steganography Framework (GAN-ASF) is presented in this study, and it can automatically learn to provide better cover audio for message embedding. The suggested framework's training architecture comprises a generator, a discriminator, and a steganalyzer learned using deep learning. The Least Significant Bit Matching (LSBM) message embedding technique encrypts the secret message into the steganographic cover audio, which is then forwarded to a trained steganalyzer for misinterpretation as cover audio. After performing the training, stenographic cover audio has been generated for encoding the secret message. Here, Markov model of co-frequency sub images to generate the best cover frequency sub-image to locate an image's hidden payload. Steganographic cover audio created by GAN-ASF has been tested and found to be of excellent quality for embedding messages. The suggested method's detection accuracy is lower than that of the most current state-of-the-art deep learning-based steganalysis. This payload placement approach has considerably increased stego locations' accuracy in low frequencies. The test results GAN-ASF achieves a performance ratio of 94.5 %, accuracy ratio of 96.2 %, an error rate of 15.7 %, SNR 24.3 %, and an efficiency ratio of 94.8 % compared to other methods.

Audio-lossless robust watermarking against desynchronization attacks

Audio-lossless robust watermarking (ALRW) is a new research issue in the field of information hiding, which can resist those content-preserving manipulations while can restore the cover audio files completely in case of no attacks. Most ALRW countermeasures proposed in the literature usually focus on additive noise-like manipulations such as MP3 compression, low-pass filtering and Gaussian additive noise, but few are resistant to challenging desynchronization attacks such as random cropping and time-scale modification (TSM). The main reason is that the exploited robust features in the existing ALRW algorithms are related to the sample position. In this paper, we propose an ALRW method by efficiently embedding a watermark into the histogram shape of the low-frequency component after the discrete wavelet transform (DWT) and reversibly hiding the distortion due to the robust watermark embedding as the compensation information for restoration of the cover audio file. The use of the histogram shape in the DWT low-frequency component as the exploited robust feature is: 1) mathematically invariant to TSM; 2) robust to random cropping attacks since an audio clip before and after the cropping often has similar distribution, and 3) resistant to interpolation errors during time scaling and those common audio processing operations. To reduce the compensation information, the robust watermarking process is elaborately and luminously designed to represent the difference between the original and the robust watermarked audio. As a result, an ALRW system against desynchronization attacks is achieved with satisfactory performance. Experimental results show that the proposed ALRW scheme can effectively reduce the compensation information and provide strong robustness to those common manipulations including TSM, random cropping, MP3 lossy compression and other noise-like manipulations. In case of no attacks, the cover audio signal can be recovered without any loss.

Enhancing LSB Using Binary Message Size Encoding for High Capacity, Transparent and Secure Audio Steganography–An Innovative Approach

We propose a novel LSB-BMSE method that enhances LSB audio steganography. It uses an innovative mechanism, Binaries of Message Size Encoding (BMSE), to embed a secret message after hiding its size in random samples. First, the secret message is compressed using Huffman coding, then encrypted by AES-128. The audio cover is split into number of blocks depending on secret message size. A secure key <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{BMSE}$ </tex-math></inline-formula> , output from the BMSE mechanism, is used to embed the secret message in random blocks and bytes adaptively according to its size. It is implemented using MATLAB and standard parameters: Perceptual Evaluation of Speech Quality and NIST Statistical Test Suite were used to measure imperceptibility between cover & stego audios and randomness of BMSE mechanism respectively. The fidelity was tested using Mean Square Error, Peak Signal to Noise Ratio and Signal-to-Noise Ratio. Comprehensive experiments on widely used metrics demonstrate that LSB-BMSE significantly surpasses existing methods in terms of hiding capacity and imperceptibility. Moreover, LSB-BMSE shows resistance to brute force attacks and statistical analysis. Although it was robust towards re-sampling attacks, nevertheless was not vigorous towards noise nor LSB attacks. The key <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{BMSE}$ </tex-math></inline-formula> complies with Kerckhoff’s principle and exhibits avalanche criteria. The tested proposed LSB-BMSE proclaimed its effectiveness.

Utilisation of audible steganography to organise and analyse the text within WAV files

This project seeks to encrypt audio cover files and create temporal domain audio steganography, an audio file with hidden options and text. The mean square error (MSE), mean absolute error (MAE), signal-to-noise ratio (SNR), and cross-correlation analysis identify audio stream text data by comparing 8-bit and 16-bit pulse-code modulation (PCM) audio. This study estimates how many characters can be added to an audio file without changing its structure. MP3's bit rate is audible. Audio steganography is a secure, cost-effective approach to encrypting network data. It's useful for steganography due to low noise distortion. Undetectable embedding is preferable. The suggested technique improves accuracy at low embed levels, according to testing. The suggested strategy delivers the highest peak signal-to-noise ratio (PSNR) with hidden information in the first, second, and third least-significant bit (LSBs). Three LSB had 98% accuracy and the lowest false alarm rate (less than 5%). Experiments reveal that this study's method extracts audio. The recommended method is hard and capable. Novel audio file steganography is imperceptible and recovers messages, and text message length affects robustness.

Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding

Many applications work by processing either small or big data, including sensitive and confidential ones, through computer networks like cloud computing. However, many systems are public and may not provide enough security mechanisms. Meanwhile, once the data are compromised, the security and privacy of the users will suffer from serious problems. Therefore, security protection is much required in various aspects, and one of how it is done is by embedding the data (payload) in another form of data (cover) such as audio. However, the existing methods do not provide enough space to accommodate the payload, so bigger data can not be taken; the quality of the respective generated data is relatively low, making it much different from its corresponding cover. This research works on these problems by improving a prediction error expansion-based algorithm and designing a mirroring embedded sample scheme. Here, a processed audio sample is forced to be as close as possible to the original one. The experimental results show that this proposed method produces a higher quality of stego data considering the size of the payloads. It achieves more than 100 dB, which is higher than that of the compared algorithms. Additionally, this proposed method is reversible, which means that both the original payload and the audio cover can be fully reconstructed.

Digital Text Security with Steganography Least Significant Bit and Audio Feature Extraction

The study developed a digital text security system by inserting the classified message or text into digital audio (stego audio) with the least significant bit (LSB) steganography and audio feature extraction. The text bits were inserted into each cover audio frame based on the value of feature extraction energy and zero-crossing rate (ZCR). The study has demonstrated that the least significant bit and audio feature extraction can cover the digital text in stego audio and then be extracted back to the original digital text. There is a small difference in feature extraction energy value between stego audio and cover audio. While there is no difference in feature extraction zero-crossing rate between stego audio and cover audio. The study also measured signal-to-noise ratio (SNR); the detected noise in stego audio because of the change in the least significant bit. The SNR is 111.97 – 130.00 dB.

Studying Audio Capacity as Carrier of Secret Images in Steganographic System

Steganography art is a technique for hiding information where the unsuspicious cover signal carrying the secret information. Good steganography technique must be includes the important criterions robustness, security, imperceptibility and capacity. The improving each one of these criterions is affects on the others, because of these criterions are overlapped each other. In this work, a good high capacity audio steganography safely method has been proposed based on LSB random replacing of encrypted cover with encrypted message bits at random positions. The research also included a capacity studying for the audio file, speech or music, by safely manner to carrying secret images, so it is difficult for unauthorized persons to suspect presence of hidden image. Measures calculations of SNR, SNR segmental, SNR spectral, MSE and correlation show that, audio music cover file (2channales) is the safest uses as arrier with replace the 9 number of LSB without noticeable noise. Bits of secret message can be hiding capacity reach up to 28 % of the total music cover audio size and the three type's measures of SNR are 32, 28 and 31 dB. For speech cover audio the replacing LSB is safely uses at LSB bits number 6, where the hiding capacity is reach up to 37 % of size speech cover audio at 37, 36 and 39 dB for three type's measures of SNR. Correlation of cover samples was did not effected as a result of hiding secret image, where its value is up to 0.99 for all hiding operations.

Medical images transmission over Wireless Multimedia Sensor Networks with high data rate

This paper presents several proposed scenarios for medical image transmission over Wireless Multimedia Sensor Networks with multi-level security tools and Unequal Error Protection (UEP) technique to provide highly secure and improved medical images transmitted over the wireless link. The proposed scenario is based on embedding the medical images with audio watermarking technique. The audio signal is presented as a medical report for the embedded medical image. The audio watermarking in this paper used the Singular Value Decomposition technique to hide the image as a watermark into the audio cover signal to form the watermarked signal. This combined signal is transmitted using the UEP technique by employing different error control schemes and different packet lengths to enhance the quality of extracting encrypted/unencrypted medical images. The results show that the quality of the extracted medical images with encoded packets is better, also, the encryption of different error control schemes employing with the image enhances the extracted image and its quality.

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.

Securing Technique Using Pattern-Based LSB Audio Steganography and Intensity-Based Visual Cryptography

With the increasing need of sensitive or secret data transmission through public network, security demands using cryptography and steganography are becoming a thirsty research area of last few years. These two techniques can be merged and provide better security which is nowadays extremely required. The proposed system provides a novel method of information security using the techniques of audio steganography combined with visual cryptography. In this system, we take a secret image and divide it into several subparts to make more than one incomprehensible sub-images using the method of visual cryptography. Each of the sub-images is then hidden within individual cover audio files using audio steganographic techniques. The cover audios are then sent to the required destinations where reverse steganography schemes are applied to them to get the incomprehensible component images back. At last, all the sub-images are superimposed to get the actual secret image. This method is very secure as it uses a two-step security mechanism to maintain secrecy. The possibility of interception is less in this technique because one must have each piece of correct sub-image to regenerate the actual secret image. Without superimposing every one of the sub-images meaningful secret images cannot be formed. Audio files are composed of densely packed bits. The high density of data in audio makes it hard for a listener to detect the manipulation due to the proposed time-domain audio steganographic method.

Adaptive Audio Steganography Based on Improved Syndrome-Trellis Codes

Syndrome-Trellis Code (STC) is a near-optimal convolutional method for adaptive steganography. Hitherto, the existing adaptive steganography commonly depends on the carefully designed distortion cost function, which controls the embedding position of the message in the cover signal. From another point of view, we implement adaptive steganography by improving the STC coding process (named Adaptive-STC). The parity-check matrix is the key to the encoding and extraction process of STC. In this work, we prove that the average embedding change probability of corresponding elements can be changed by adjusting its submatrix. Following that, we specially design an adaptive parity-check matrix to replace the designed distortion cost to restrict the embedding position. The generation of the adaptive parity-check matrix can be formulated as a multi-constrained integer programming problem in which the width of the submatrix is allocated at a fixed height. To solve this particular problem, we propose a targeted intelligent optimization algorithm (named GOAS) that can adaptively generate the parity-check matrix according to different audio cover. The experimental results show that the proposed method outperforms the state-of-the-art adaptive steganography with reduced embedding changes and improved audio quality while ensuring the ability against steganalysis.

DESIGN OF SECURE IMAGE DATA HIDING FOR SECURITY PROTOCOL USING AES AND LSB ALGORITHM

This paper presents a critical analysis on new and original proposed algorithm based on hiding any data has been used that overcomes the disadvantages of the existing algorithms and helps to provide less similarity between cover image and stego image and obtain accuracy upto 69.6 percentage and increases its robustness using metrics called mean square error and peak signal to noise ratio. In the wireless environment cryptography suffers from various spyware programs that shows corrupted secret information to innocent users who uses image steganography services from user. In our proposed prototype helps to authenticate the sender to make the unnoticeable image from original image. In our proposal work discovers a secure authentication communication model would able to cover multimedia data like first text to be hide, second image to be hide and third audio secret data to be hide in cover image without much noticed to any user in between network. In order to provide additional security to this model we incorporate AES encryption scheme where secret message is encrypted and hidden in the cover audio. The proposed approach uses DCT coefficient computation and AES encryption scheme. An extensive experimental study is carried based on different test cases and evaluated against state-of-art techniques. The experimental study shows that the proposed approach achieves better performance for audio steganography. Keywords: AES, steganography, cover image, hiding image, network, digital data.