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

Encrypting an audio file based on integer wavelet transform and hand geometry

A new algorithm suggested for audio file encryption and decryption utilizing integer wavelet transform to take advantage of the property for adaptive context-based lossless audio coding. In addition, biometrics are used to give a significant level of classification and unwavering quality because the procedure has numerous qualities and points of interest. The offered algorithm utilized many properties of hand geometry estimations as keys to encode and decode the audio file. Many tests were carried out on a set of audio files and quality metrics such as mean square error and correlations were calculated which in turn confirmed the efficiency and quality of the work

DWT-SMM-based audio steganography with RSA encryption and compressive sampling

Problems related to confidentiality in information exchange are very important in the digital computer era. Audio steganography is a form of a solution that infuses information into digital audio, and utilizes the limitations of the human hearing system in understanding and detecting sound waves. The steganography system applies compressive sampling (CS) to the process of acquisition and compression of bits in binary images. Rivest, Shamir, and Adleman (RSA) algorithms are used as a system for securing binary image information by generating encryption and decryption key pairs before the process is embedded. The insertion method uses statistical mean manipulation (SMM) in the wavelet domain and low frequency sub-band by dividing the audio frequency sub-band using discrete wavelet transform (DWT) first. The optimal results by using our system are the signal-to-noise ratio (SNR) above 45 decibel (dB) and 5.3833 bit per second (bps) of capacity also our system has resistant to attack filtering, noise, resampling and compression attacks

Image steganography based on DNA sequence translation properties

Digital communication has become a vital part of daily life nowadays, many applications are using internet-based communication and here the importance of security rose to have a secure communication between two parties to prevent authorized access to sensitive data. These requirements led to a number of research in information security that has been done in the past two decades. Cryptography and steganography are the two main methods that are being used for information security. Cryptography refers to techniques that encrypt a message to be sent to a destination using different methods to be done. On the other hand, steganography is the science of hiding information from others using another cover message or media such as image, audio, video, and DNA sequence. This paper proposed a new method to hide information in an image using the least significant bit (LSB) based on Deoxyribonucleic Acid (DNA) sequence. To accomplish this, the proposed scheme used properties of DNA sequence when codons that consist of three nucleotides are translated to proteins. The LSB of two pixels from the image are taken to represent a codon and then translate them to protein. The secret message bits are injected into codons before the translation process which slightly distorts the image and makes the image less suspicious and hard to detect the hidden message. The experimental results indicate the effeteness of the proposed method

Adopt an optimal location using a genetic algorithm for audio steganography

With the development of technologies, most of the users utilizing the Internet for transmitting information from one place to another place. The transmitted data may be affected because of the intermediate user. Therefore, the steganography approach is applied for managing the secret information. Here audio steganography is utilized to maintain the secret information by hiding the image into the audio files. In this work, discrete cosine transforms, and discrete wavelet transform is applied to perform the Steganalysis process. The optimal hiding location has been identified by using the optimization technique called a genetic algorithm. The method utilizes the selection, crossover and mutation operators for selecting the best location. The chosen locations are difficult to predict by unauthorized users because the embedded location is varied from information to information. Then the efficiency of the system ensures the high PSNR, structural similarity index (SSIM), minimum mean square error value and Jaccard, which is evaluated on the audio Steganalysis dataset

STEGANALISIS AUDIO BERBASIS DERIVATIVE SPECTRAL PADA DOMAIN FOURIER

Abstraksi Steganografi data digital saat sekarang ini tidak hanya digunakan untuk kepentingan yang bersifat tidak melanggar hukum namun juga sudah digunakan sebagai cara untuk melakukan tindak kejahatan. Untuk ini perlu adanya pengawasan terhadap pertukaran data untuk mengindikasi apakah dalam suatu objek terdapat pesan rahasia yang berbahaya atau tidak. Steganalisis adalah salah satu metode pada komputer forensik yang digunakan untuk mendeteksi apakah sebuah objek berisi pesan tersembunyi atau tidak. Pada penelitian yang dilakukan oleh Min Ru dikembangkan steganalisis audio berbasis pada kumpulan fitur distorsi. Dan ada pula yang berfokus pada mel-cepstrum yang dikembangkan oleh Kraetzer. Pada tugas akhir ini akan dilakukan steganalisis pada media audio dengan menggunakan Fourier Spectrum dan dilakukan pengembangan dengan menambahkan kombinasi feature-set guna mengetahui kombinasi mana yang paling mempengaruhi deteksi dan menerapkan metode framing yang membagi sampel audio menjadi beberapa bagian untuk memeriksa setiap bagian sampel yang menjadi lokasi penyimpanan pesan. Lalu kemudian Support Vector Machine (SVM) digunakan sebagai classifier untuk menentukan indikasi stego audio. Dengan menerapkan metode ini dibangun sistem mampu mendeteksi stego audio dengan akurasi deteksi tertinggi adalah 78% namun AUC yang kurang memuaskan hanya 51%. Kata kunci: Steganografi, steganalisis, fourier spectrum, derivative spectral, SVM

Forensic Analysis of Android Steganography Apps

The processing power of smartphones supports steganographic algorithms that were considered to be too computationally intensive for handheld devices. Several steganography apps are now available on mobile phones to support covert communications using digital photographs. This chapter focuses on two key questions: How effectively can a steganography app be reverse engineered? How can this knowledge help improve the detection of steganographic images and other related files? Two Android steganography apps, PixelKnot and Da Vinci Secret Image, are analyzed. Experiments demonstrate that they are constructed in very different ways and provide different levels of security for hiding messages. The results of detecting steganography files, including images generated by the apps, using three software packages are presented. The results point to an urgent need for further research on reverse engineering steganography apps and detecting images produced by these apps

High Capacity Audio Steganography Based on Contourlet Transform

The science of hiding information behind other cover media file is known steganography. Audio steganography means that a secret message is hidden by embedding it in an audio file. This paper presents a new audio steganography approach that is used the contourlet transform to hide a speech and image in an audio signal. The cover audio signal is modified to be suitable as input to contourlet transform and then secret data embed to the subbands of contourlet transform. The results showed high hiding capacity of data up to 90% of cover audio file size. In addition, performance analysis by measures factors: Normalized Correlation (NC), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) appears good quality results for both stego and secret data