Cover Audio Research Articles

ENGLISH

Steganography is the art of hidden inscription, used prominently to cover text, image, video and audio. The persistence of steganography is to hide the presence of a message from a third party. The intention of this paper is to achieve statistical security without degrading the stego image quality and to improve undetectability. This paper conceals the secret image behind the original image to obtain the stego image. The extraction of inversion pattern, rotation pattern and invariant local binary pattern is preceded to calculate the distortion of cover image. The obtained image is scrambled in order to distribute the pixels uniformly. To reduce the embedding impact of image and to improve the statistical security, Syndrome Trellis Code (STC) is employed which mainly uses the convolutional code with Viterbi algorithm. The steganography application is realized in STC technique by covering the binary image over the original image. The resultant stego image is obtained after proceeding with the STC technique. The proposed technique is simulated in MATLAB and implemented in Spartan 3 FPGA, interfaced with VGA controller to ensure in real time applications.

Using Rubik’s Cube and a Modified LSB for Audio Steganography

Nowadays steganography plays an important role in information security. There are
 various techniques to apply steganography; one such technique is Least Significant
 Bit (LSB). Due to consecutive embedding of data, LSB is highly vulnerable to data
 retrieval and manipulation. This study proposes a mixture between Rubik’s cube
 principle to scramble the audio data, and a modified LSB technique to hide the secret
 data. The modified LSB technique includes embedding using only irredundant bits of
 binary representation of each character in the secret message, and hiding in the lowest
 sample between two consecutive samples of the cover audio. The used technique
 makes the retrieval of secret message harder because it adds two levels of protection
 (scrambling, and hiding in the lowest sample) against the attempts of obtaining data;
 and makes the embedded data imperceptible.

A multi-threshold based audio steganography scheme

Human auditory system is highly sensitive that can detect or discriminate subtle changes in an audio. The direct least significant bit (LSB) substitution scheme, often used in image steganography, cannot be effective for audio steganography to provide secrecy needed for many audio applications, because the embedded secret bits in the LSBs of the audio samples in a silent or near silent segment of an audio can produce strong hissing noise when played. In this work, governed by an error criterion, we present a multi-threshold-based scheme for audio steganography. In order to evade detection by means of any statistical or perceptual analysis of the resultant audio, the threshold values are derived from an error criterion that can be used to limit the number of bits to be embedded in an audio sample. To defend against retrieval of bits by an adversary as well as to dissipate any pattern in the resultant audio, a scheme for chaotic distribution of the secret message bits in the cover audio is also proposed. In comparison with most recently proposed schemes in literature, our proposed scheme exhibits better performance against statistical and perceptual analysis for the same embedding capacity. Our proposed scheme can be used to increase the capacity of the cover audio in a controlled manner by adjusting the error criterion.

Concurrent hardware architecture for dual-mode audio steganography processor-based FPGA

Recently, audio steganography has become an important covert communications technology. This technology hides secret data in a cover audio without perceptual modification of the cover audio. Most of the existing audio steganography techniques are unsuitable for real-time communication. Although field programmable logic array (FPGA) technologies offer parallel processing in hardware that can improve the speed of steganographic systems, the research activities in this area are very limited. This paper presents a parallel hardware-architecture for dual-mode audio steganography (DMAS) based FPGA technology. The proposed DMAS reconfigures the same hardware blocks in both hiding and recovery modes to reduce the hardware requirements. It has been successfully implemented on a Xilinx XC6SLX16 FPGA board to occupy only 97 slices. Furthermore, it processes data simultaneously at an operating frequency of up to 58.82MHz and accomplishes full message retrieval at an embedding rate of 25% with an audio quality above 45dB in terms of signal to noise ratio.

Hiding Encrypted Data in Audio Wave File

at a time of globalization in which the whole planet become look like a small village and transport information through it with a push button in keyboard and at a time of huge evaluation in the digital world and for the millions of users to international net and some application importance, let us to use both cryptography and steganography as a means to protect the information from hacking and interlopers. Combinations of steganography and cryptography have been made in this paper to increase the level of security and to make the system more complex to be defeated by attackers. This paper presents a method of ciphering and hiding information into a cover of audio wave file. In the first step, the text is converted to its equivalent ASCII code. Text is scrambled using DES (Data Encryption Standard) technique according to secret key then the cipher text is embedded inside a cover audio wave file using time and frequency domain. The modeled system builds of a secret key steganographic system, which embeds certain text after scrambling into random positions inside audio wave file and the other technique used in this paper to analyze the cover audio into its frequency components is wavelet transform using Haar filter as a basis function. Tests were carried out to evaluate the performance of the modeled algorithm and results in successful hiding. From the simulation results on the time domain, it is noticed that value of Signal to Noise Ratio (SNR) will decrease as the number of bits in the audio wave file to be replaced by the message increase and noticed that the value of SNR in frequency domain is higher than in time domain, so hiding in frequency domain is better than hiding in time domain.

A Novel and High Capacity Audio Steganography Algorithm Based on Adaptive Data Embedding Positions

In this study, a novel and high embedding capacity audio steganography scheme based on Lifting Wavelet Transform (LWT) and adaptive embedding positions is proposed. Specifically, the message data is inserted in the imperceptible positions that chosen from the coefficients of detail sub-bands taking advantage of our proposed Weighted Block Matching (WBM). The WBM is preceded by preparing the cover audio in order to select the bits-positions that can possibly be used for embedding from each detail coefficient based on coefficient amplitude then copy the contents of the selected bits-positions and arrange them in blocks of bits. Also, the message data is arranged in blocks of bits after preprocessed and encrypted. The WBM computes the matching between each message block and whole extracted cover blocks to find the similarity between them. This process help to provide optimal locations to hide the message blocks. These locations are considered as a stego-key that is ciphered and hided within the final detail sub-band which is specified for this purpose. The proposed approach attains higher security than other fixed embedding positions approaches because the random positions for the embedded message blocks based on adaptive selection for embedding positions. Experimental results show that the proposed technique is not only has very high embedding capacity (exceed 300 kbps) with excellent transparency (above 35 dB for the cover to noise ratio), but also achieve lossless massage retrieved. Comparisons with the related audio steganography algorithms also show that our proposed scheme outperforms all the selected algorithms.

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.

A MULTILEVEL SECURITY SCHEME USING CHAOS BASED ENCRYPTION AND STEGANOGRAPHY FOR SECURE AUDIO COMMUNICATION

Steganography is the practice of encoding secret in formation in indiscernible way. Audio steganography is the science of hiding some secret text or audio information in a host audio. T his paper contains audio stegnography using bit mod ification of time domain audio samples which is a well known simple technique for multimedia data embedding with potential for large payload But before applying stegnography or hiding secret audio in cover audio a chaos based encryption is performed on secret aud io data in order to increase the security against steganalysis. And the bit modification technique is also not the simple LSB (least significant bit) modification tec hnique. It contains two approaches which make the steganalysis more difficult as compa re to simple LSB technique. First approach is with selection of bits of sample for embedding secret audio while the other approach is to use the compliment of the secret audio before hiding it in the host message. The improvised proposed approach works against steg analysis and decreases the probability of secret audio being extracted by an iChaos based encryption is used to secure saudio in case the stegnography technique breaks.

Lossless Audio Steganography based on Lifting Wavelet Transform and Dynamic Stego Key

This paper presents a new lossless audio steganography approach based on Integer-to-Integer Lifting Wavelet Transform (Int2Int LWT) and Least Significant Bits (LSBs) substitution. In order to increase the security level a simple encryption with adaptive key has been proposed. The experimental results show that this approach has excellent transparency (above 45 dB of signal to noise ratio) with fixed high embedding capacity (25% from the audio cover signal size) and full recovery for the hidden secret message. Furthermore, robustness tests show immunity of the method against additive noise and perceptual statistical analysis. The proposed hiding and recovery procedures are simple and symmetry; therefore it can be easily used for real-time covert communication.

Detection of MP3Stego exploiting recompression calibration-based feature

MP3Stego is a typical steganographic tool for MP3 audio. Once the cover audio is unavailable, it is hard to distinguish between background noise and steganographic distortion. In this work, the MP3Stego algorithm has been analyzed from a warden's perspective. It is observed that the number of bits in the bit reservoir will be disturbed when the secret message is embedded. In addition, a reliable estimation of cover audio is obtained by the proposed recompression calibration. The calibrated features are classified with support vector machine technique. Experimental results show that the proposed scheme is effective and gets good performance, especially when the embedding rate is not less than 0.01 %. The results also shows that the proposed scheme can achieve lower false positive rate comparing to the existing algorithms.

Content integrity and non‐repudiation preserving audio‐hiding scheme based on robust digital signature

ABSTRACTCurrent secure communication schemes do not take together traffic security and data security (content integrity and non‐repudiation) of the secret message into consideration, making the content prone to blind tampering and compromised party cheating attacks. In this paper, we present a scheme that hides secret audio in cover audio on the basis of robust digital signature to preserve not only hidden communication but also content integrity and non‐repudiation of the secret audio. Furthermore, instead of traditional binary authentication that only outputs yes or no, the authentication of our scheme is flexibly measurable, and the measurement value is in correspondence with the sense of human hearing precisely. Experimental results show that the proposed scheme provides highly robust authentication against content‐preserving degradations with 99.03% of test audios having the strongest authenticity (1.00) and high level of distinct authentication between content‐destructive degradations with 95.01% of test audios having relatively weak authenticity (less than 0.15). As the authentication is flexibly measureable, there is no false alarm in the semantic aspect. Copyright © 2013 John Wiley & Sons, Ltd.

A Hybrid Model for Secure Data Transfer in Audio Signals using HCNN and DD DWT

In today’s world, there are a number of cryptographic and steganography techniques used in order to have secured data transfer between a sender and a receiver. In this paper a new hybrid approach that integrates the merits of cryptography and audio steganography is presented. First, the original message is encrypted using chaotic neural network and the resultant cipher text is embedded into a cover audio using Double Density Discrete Wavelet Transform (DD DWT). The resultant stego audio is transmitted to the receiver and the reverse process is done in order to get back the original plain text. The proposed method presents a Steganography scheme along with the cryptography scheme which enhances the security of the algorithm.

Feature Based Audio Steganalysis (FAS)

Taxonomy of audio signals containing secret information or not is a security issue addressed in the context of steganalysis. A cover audio object can be converted into a stego-audio object via different steganographic methods. In this work the authors present a statistical method based audio steganalysis technique to detect the presence of hidden messages in audio signals. The conceptual idea lies in the difference of the distribution of various statistical distance measures between the cover audio signals and their denoised versions i.e. stego-audio signals. The design of audio steganalyzer relies on the choice of these audio quality measures and the construction of two-class classifier based on KNN (k nearest neighbor), SVM (support vector machine) and two layer Back Propagation Feed Forward Neural Network (BPN). Experimental results show that the proposed technique can be used to detect the small presence of hidden messages in digital audio data. Experimental results demonstrate the effectiveness and accuracy of the proposed technique.

Research on Uncertainty of audio and Video Information Hiding Based on Semantic and Statistical Moment

nowadays, audio and video media data is already facilitates generation, transmission, storage and circulation on the global scale. Audio and video data is geometrically fast as the rate of growth, the video data processing and analysis have lagged behind the pace of development in the growth of data, resulting in large amounts of data is wasted. Therefore, it becomes an urgent need for efficient retrieval of video data content. Accordingly the SS hiding effectively, verify the presence of the secrete message in an important issue. In this paper we present two statistical analysis algorithms for SS hiding. Both the two methods are based on machine learning theory and discrete wavelet transform (DWT), which adopts the classification technology. In the algorithm I, we introduce Gaussian mixture model (GMM) and generalize Gaussian distribution (GGD) to character the probability distribution of wavelet sub-band. Then the absolute probability distribution function (PDF) moment is extracted as feature vectors. We use GMM to model the probability distribution of wavelet coefficient and calculate the absolute moment of statistical distribution as feature vector of each sub-band for statistical analysis. In the algorithm II, we propose distance metric between GMM and GGD of wavelet sub-band to distinguish cover. The experiment results of both two proposed classification algorithms may obtain better detecting performance. The probability distribution model takes GMM and GGD. We use de-noising method to get the estimation of cover audio, and then use four distances metric to measure the distortion.

Information Hiding in Audio Signals

ABSTRACT Steganography is the practice of encoding secret information in indiscernible way. Audio Steganography is a technique used to transmit hidden information by modifying an audio signal in an imperceptible manner. It is mainly required for increasing security in transferring and archiving of audio files. Modern advances in computer, communication and signal Steganography complements procesrather than replaces encryption by adding another layer of security- of steganography. These advances have broadened it is much more difficult to decrypt a message if it is not known that there is a message. The basic idea of the paper is to present methods that hides information (audio, image and text) in cover audio using Least Significant Bit (LSB) coding method along with encryption so as to increase the security. Two novel methods have been proposed in this paper, one is considering parity of the digitized samples of cover audio and the other is considering the XOR operation. A novel method which is an extension to the XOR method that uses multiple LSB‟s for data embedding is also proposed. Experimental results are presented in this paper to demonstrate the effectiveness of the proposed methods. In addition, subjective listening tests are performed and the perceptual quality of the stego audio signal is found to be high.

Audio steganalysis of spread spectrum information hiding based on statistical moment and distance metric

Audio information hiding has attracted more attentions recently. Spread spectrum (SS) technique has developed rapidly in this area due to the advantages of good robustness and immunity to noise attack. Accordingly detecting the SS hiding effectively and verifying the presence of the secrete message are important issues. In this paper we present two steganalysis algorithms for SS hiding. Both the two methods are based on machine learning theory and discrete wavelet transform (DWT). In the algorithm I, we introduce Gaussian mixture model (GMM) and generalize Gaussian distribution (GGD) to character the probability distribution of wavelet sub-band. Then the absolute probability distribution function (PDF) moment is extracted as feature vectors. In the algorithm II, we propose distance metric between GMM and GGD of wavelet sub-band to distinguish cover and stego audio. Four distance metrics (Kullback-Leibler Distance, Bhattacharyya Distance, Earth Mover's Distance, L2 Distance) are calculated as feature vectors. The support vector machine (SVM) classifier is utilized for classification. The experiment results of both two proposed algorithms can achieve better detecting performance. Even when embedding strength gets 0.0005, the correct detection rate can reach up to 90%. Its simplicity and extensibility indicate further application in other audio steganalysis.

Evaluation of Wavelet Transform Audio Hiding

Audio hiding is method for embedding information into an audio signal. It seeks to do so in a robust Fashion, while not perceivably degrading the host signal (coves audio). Hiding data in audio signals presents & varicty of challenges: due in part to the wider dynamic and differential range of the Human Auditory System (HAS) as compared to other senses. Transform are usually used for robust audio hiding (audio watcrmarking). But, the audio hiding process is affected by the type of transform used. Therefore, this paper presents an evaluation of wavelet transform hiding in comprison with sclccted types of transforms (Walsh transform and cosine transform) hiding. In order to generate the audio stegecover, the research concludce (Wavelet, Walsh, or Cosine) transform of the audio cover, replacing some transformed cover cocfficients with secret audio message coefficients, and inverse (Wavelet, Walsit, or Carsine Irannom for audio cover with replaced coefficients. While, the extracting method concludes (Wavelel, Walsh, or Cosine runsform of the stego ove18 and extracting the secrecy extracted Audio message.