Stego Audio Research Articles

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)

Hiding Capacity and Audio Steganography Model Based on LSB in Temporal Domain

Background: Researchers for data hiding using the various encrypting techniques to improve the security of transmission of confidential information through an unsecured channel have been carried out. Methods: In this paper, a new higher recognition Least Significant Bit (LSB) audio data concealment is suggested in this framework. This technique is used to embed the hidden audio into cover audio of the same size. Results: The altered speech file appears as the original carrier file after embedding the secret message. This model strengthens hiding capability and audio quality. Conclusion: The strategy outperforms similar studies by enhancing hiding capability up to 30% and preserving stego audio transparency with the SNR value at 72.2 and SDG at 4.8.

Backward movement oriented shark smell optimization-based audio steganography using encryption and compression strategies

Audio steganography is the process of hiding secret information such as images, text, or video into the audio file. Data hiding in audio signals has been utilized in diverse applications like secret communication, hiding of data that may affect the safety and security of personnel and governments. Most of the recently introduced audio steganography approaches are content non-adaptive that may suffer low embedding capability and poor security. This paper proposes the audio steganography model for secure audio transmission during communication based on the meta-heuristic concept. In the embedding phase, the optimized instant selection is made for embedding the secret message (image) into the input audio. Further, the secret message to be embedded is encrypted using the optimized 2D-Logistic Chaotic Map. Here, the development of optimal instant selection and the optimized 2D-Logistic Chaotic Map are performed by the improved Shark Smell Optimization (SSO) called Backward Movement oriented SSO (BM-SSO). Once the secret message is encrypted, the compression of the message is done by Modified Huffman Encoding (MHE). Finally, the extraction phase uses the optimized 2D-Logistic Chaotic Map-based decryption. The proposed audio steganography by optimized instant selection for audio embedding minimizes the distortion error between the stego audio and original audio in terms of Mean Squared Error (MSE), and optimized 2D-Logistic Chaotic Map-based encryption aims to maximize the entropy of cipher image. The performance of the developed model has been estimated extensively against attacks, and simulation results are presented to prove the robustness of the proposed algorithm.

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.

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.

Hiding data in audio files: A smoothing-based approach to improve the quality of the stego audio.

In this digital era, transmitting data through a computer network has become common. Moreover, some applications have also been developed to do it. Nevertheless, users may not be aware of the security aspect of this data transmission, which can lead to disclosing this private message. In a case when a sensitive message is the object to transmit, a security mechanism should be applied. Data hiding is one of the methods introduced to work for this issue. In this algorithm, the message is embedded into the cover, such as an audio file, before being transmitted; on the other side, the recipient extracts it. However, the size of the message and the quality of the resulted stego data are still challenging. In this paper, we focus on these two problems by considering some factors: embedding space, embedding process, reducing, and smoothing steps. Firstly, the audio signal is discretized to obtain audio samples. Next, these samples are interpolated to provide spaces for hiding the secret. Considering that the quality of the generated stego audio is likely to drop, reducing and smoothing steps are designed. The experimental results show that this approach can improve the quality of the stego based on the specified payload capacity. That is, there is an increase in PSNR value of at least 10 dB, depending on the payload size and the methods.

A Secure AMR Fixed Codebook Steganographic Scheme Based on Pulse Distribution Model

Adaptive multi-rate (AMR), a popular audio compression standard, is widely used in mobile communication and mobile Internet applications and has become a novel carrier for hiding information. To improve the statistical security, this paper presents a steganographic scheme in the AMR fixed codebook (FCB) domain based on the pulse distribution model (PDM-AFS), which is obtained from the distribution characteristics of the FCB value in the cover audio. The pulse positions in stego audio are controlled by message encoding and random masking to make the statistical distribution of the FCB parameters close to that of the cover audio. The experimental results show that the statistical security of the proposed scheme is better than that of the existing schemes. Furthermore, the hiding capacity is maintained compared with the existing schemes. The average hiding capacity can reach 2.06 kbps at an audio compression rate of 12.2 kbps, and the auditory concealment is good. To the best of our knowledge, this is the first secure AMR FCB steganographic scheme that improves the statistical security based on the distribution model of the cover audio. This scheme can be extended to other audio compression codecs under the principle of algebraic code excited linear prediction (ACELP), such as G.723.1 and G.729.

A novel approach for audio steganography by processing of amplitudes and signs of secret audio separately

Now a days, cases of theft of important data both by employees of the organization and outside hackers are increasing day-by-day. So, new methods for information hiding and secret communication are need of today. Steganography is an option for it. Embedding a secret message into other meaningful message (cover media) without disturbing the features of the cover media is known as steganography. A novel approach for audio steganography is proposed in this paper. Here, secret message and cover media both are digital audio. Proposed approach is robust with respect to both LSB removal and re-sampling attacks. This approach adds extra layer of security because a transformation function is applied on amplitude bits of secret audio before embedding. This approach is more resistive towards white Gaussian noise addition (WGN) during transmission of stego file. The proposed approach is also suitable for embedding secret audio during real time audio communication because processing time is low while embedding capacity is high. Embedding capacity of the proposed approach is same as of conventional LSB approach because in both approaches one bit of secret is being inserted in each sample of cover audio. Standard parameters: Perceptual Evaluation of Speech Quality (PESQ) and Mean Opinion Score (MOS) are used for measuring the imperceptibility between cover audio & stego audio. For the proposed approach, PESQ and MOS are found as 4.47 and 5 that are very close to their respective highest values 4.5 and 5 when there is no attack.

IMPLEMENTASI ALGORITMA KRIPTOGRAFI RC4 DAN METODE STEGANOGRAFI AUDIO 2LSB PADA SISTEM KEAMANAN INFORMASI

Providing security and confidentiality to information is needed when exchanging information through communication networks. It is intended that the information sent by the sender can be received in full by the recipient without any interference from parties who are not interested in the information.Cryptographic techniques and steganography can be used to secure confidential messages. Building applications that combine these two techniques can provide security for confidential messages well. Security techniques that can be used are cryptographic techniques using the RC4 algorithm to secure secret messages in the form of text or images, and insert secret messages with 2LSB steganography method into audio media.The analysis carried out is the level of success of the message insertion and extraction process, process speed, stego audio attack, and audio quality. The results of 18 times insertion testing and message extraction, obtained a success percentage of 100% with different processing times depending on the size of the message being inserted. The resulting Stego audio has good quality and does not cause noise that can be heard directly by the human senses, but stego audio cannot withstand attacks that cause changes to the stego file byte value. So it can be concluded that the combination of the RC4 cryptographic algorithm and the 2LSB steganography method can secure the message properly and provide the results of decryption without any changes to the inserted message

High capacity, transparent and secure audio steganography model based on fractal coding and chaotic map in temporal domain

Information hiding researchers have been exploring techniques to improve the security of transmitting sensitive data through an unsecured channel. This paper proposes an audio steganography model for secure audio transmission during communication based on fractal coding and a chaotic least significant bit or also known as HASFC. This model contributes to enhancing the hiding capacity and preserving the statistical transparency and security. The HASFC model manages to embed secret audio into a cover audio with the same size. In order to achieve this result, fractal coding is adopted which produces high compression ratio with the acceptable reconstructed signal. The chaotic map is used to randomly select the cover samples for embedding and its initial parameters are utilized as a secret key to enhancing the security of the proposed model. Unlike the existing audio steganography schemes, The HASFC model outperforms related studies by improving the hiding capacity up to 30% and maintaining the transparency of stego audio with average values of SNR at 70.4, PRD at 0.0002 and SDG at 4.7. Moreover, the model also shows resistance against brute-force attack and statistical analysis.

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.

A new statistical attack resilient steganography scheme for hiding messages in audio files

We present a novel approach for audio steganography that preserves first-order statistical properties of cover audio after embedding a secret message. This approach can avoid detection by histogram-based or similar statistical attacks. This approach partitions the audio samples in the cover audio, which is followed by reordering of the samples in each partition through an encoding process for embedding the secret message. Partitioning of samples is governed by a specified error limit on individual samples, and the error limit is determined from signal-to-noise ratio that needs to be maintained in the stego audio to avoid detection by an automated system or human auditory system. Experimental results on effectiveness as well as on capacity are presented using 8-bit and 16-bit audio as covers. It is shown that the proposed approach can achieve high capacity while maintaining its effectiveness against attacks.

A new statistical attack resilient steganography scheme for hiding messages in audio files

We present a novel approach for audio steganography that preserves first-order statistical properties of cover audio after embedding a secret message. This approach can avoid detection by histogram-based or similar statistical attacks. This approach partitions the audio samples in the cover audio, which is followed by reordering of the samples in each partition through an encoding process for embedding the secret message. Partitioning of samples is governed by a specified error limit on individual samples, and the error limit is determined from signal-to-noise ratio that needs to be maintained in the stego audio to avoid detection by an automated system or human auditory system. Experimental results on effectiveness as well as on capacity are presented using 8-bit and 16-bit audio as covers. It is shown that the proposed approach can achieve high capacity while maintaining its effectiveness against attacks.

Audio Hiding based on Wavelet Transform and Linear Predictive Coding

In this work an efficient method for hiding a speech in audio is proposed. The features of secretspeech is extracted with LPC (Linear Predictive Coding), and these parameters embedded in audio inchaotic order. Discrete Wavelet Transform (DWT) is applied on audio frames to split the signal in high andlow frequencies. The embedding parameters are embedded in high frequency. The stego audio isperceptually indistinguishable from the equivalent cover audio. The proposed method allows hiding a sameduration of speech (secret) and audio (cover). The stego audio is subjected to objective tests such signal to noiseratio (SNR), signal to noise ratio segmental (SNRseg), Segmental Spectral SNR, Log Likelihood Ratio (LLR)and Correlation (Rxy) to determine the similarity with original audio.

A New Method to Increase the Capacity of Audio Steganography Based on the LSB Algorithm

Audio techniques have been developed for audio streaming on the internet. Using the TCP/IP protocol, audio file can be uploaded, downloaded, and transmitted through the internet. This benefit of transmission makes the interest in using audio as cover object in steganography become much stronger. Capacity which is one of the most important properties of audio steganographic methods, evaluates the amount of possible embedding data within the audio file. A new embedding technique of audio steganography is proposed to increase the carrier medium capacity for substitution additional hidden message. With respect to the performed tests, the algorithm succeeds to increase the depth of embedding layer, without having effects on the signal transparency. The maximum number of bits without significant effect on host audio signal for LSB audio steganography is 4 LSBs. The secret message bits are hidden into variable and multiple LSBs layer in this method. Experimental results show that the use of this new technique which apply 7 LSBs for data embedding in comparison the LSB standard algorithm with 4 LSBs improve data hiding capacity of carrier audio by 35% to 55%. It can be observed from listening tests that there is no significant difference between the stego audio acquired from the novel technique and the main signal.

MESSAGE GUIDED RANDOM AUDIO STEGANOGRAPHY USING MODIFIED LSB TECHNIQUE

Steganography is the science of hiding secret data in such a way that its presence can't be noticed. Techniques which hide more secret data in cover files and which doesn't affect transparency of cover signal are better one for steganographic purpose. In the proposed technique which keyless randomization is provided to insert secret information in multiple and variable LSBs. Cover signal is converted into binary format and then with the proposed algorithm binary cover signal is divided into blocks of size 8x8 with 16 bits per sub block, and then checking each sub blocks first two MSBs to find how many LSBs will be used for insertion of secret data bits. PSNR values show that there is no noticeable difference between cover audio signal and stego audio signal. Security increases due to the use of variable number of LSBs for insertion and keyless randomization are provided by counting out technique.

Message Guided Adaptive Random Audio Steganography using LSB Modification

Steganography is an art of hiding secret messages such that its very presence can’t be identified. Techniques which hide more secret data in cover files are better and they will be more better if they doesn’t affect transparency of cover signal and make steganalysis difficult. We propose a technique which provide keyless randomization to insert secret information in multiple and variable LSBs. Using this method, binary cover signal is divided into blocks of size 8x8 with 16 bits per sub block, and then checking each sub block’s first two MSBs to find how many LSBs will be used for insertion of secret data bits. Results show that there is no noticeable difference between cover audio signal and stego audio signal. Capacity and security increases due to the use of variable number of LSBs for insertion and keyless randomization provided by counting out technique. General Terms Security, robustness, algorithm, capacity, transparency.

Application of a generalized difference expansion based reversible audio data hiding algorithm

In this paper, a generalized difference expansion based reversible audio data hiding algorithm is proposed and a recommended application is introduced. The proposed algorithm is built on top of a recently proposed reversible image watermarking scheme. The main challenges to customize an image watermarking scheme to work for audio domain are the differences between the structures of image and audio, the target of the image watermarking scheme that based on is a two-dimensional 8-bit grayscale image and the target now is a one-dimensional 16-bit quantization audio waveform. The dimension issue can be solved by small modifications, while the difference between the sizes of image pixel and waveform sample is handled by the proposed intelligent partitioning algorithm, which partition the audio waveform into two portions, so that the image watermarking scheme based on can be applied to the audio. For the proposed algorithm, satisfactory amount of data can be hidden into the audio and the quality of the stego audio is acceptable. In average, a maximum embedding rate of 34,699 bits/sec can be achieved for the input audio, and in normal meta-data tagging and authentication embedding, SegSNR values around 30 dB can be obtained. With the proposed algorithm, the application recommended is useful in protecting audio copyright.

Objective evaluation of sound quality for attacks on robust audio watermarking

Various attacks on robust audio watermarking have been proposed. Reversible signal processing attacks, such as sampling frequency conversion, degrade sound quality of the distributed watermarked audio (stego audio) and disturb extraction of hidden data so that copyright detection systems using automated crawling are invalidated. Reversible signal processing of the attack can recover sound quality of the degraded audio data. In order to prove validity and security of audio watermarking system, analysis of the presumed attacks or reversible signal processing on stego audio, is required. However, these attacks on audio signal also degrade sound quality of commercial music where such pieces of music are considered to be not suitable for appreciation. Therefore, degradation of sound quality induced by various attacks should be taken into account to decide if the intensity of the attacks are realistic. In this study, objective audio quality measurement (PEAQ) was applied to the audio signals including typical perceptual coding, MP3, tandem MP3, MPEG4AAC, and reversible signal processing of sampling frequency conversion, noise addition, frequency shift, bandpass filtering, and echo addition. The results indicate requirements for robustness and criteria of the attacks on high quality and robust audio watermarking technology.