Audio Watermarking with Encryption

Audio Watermarking with Encryption

Water Marking

John Britto Student, SPIT Mumbai, India

M.Mani Roja Professor, TSEC Mumbai, India

Secrete Image

Audio Input

AbstractWatermarking is an effective means of hiding data, thereby protecting the data from unauthorized or unwanted viewing. Watermarks have been proposed previously as a way for protecting the data. This digital signature could discourage copyright violation, and may help determine the authenticity and ownership of an image. In this paper, audio watermarking is done with the help of Discrete Fourier Transform (DFT). To enhance the security of the watermark, pseudo noise (PN) based encryption is used.

KeywordsWater Marking; DFT; Encryption; PN sequence;

DFT

Encryption

IDFT

Figure 1: Watermarking Embedding

O/P

Image Retrieval

DFT

Audio Input

MSE

1. INTRODUCTION

   The block diagram for watermark extraction is shown in fig 2.

   Watermarking is the practice of imperceptibly altering a Work to embed a message about that Work [1]. Watermarking is becoming increasingly popular, especially for insertion of undetectable identifying marks, such as author or copyright information to the host signal. Watermarking may probably be best used in conjunction with another data-hiding method such as steganography, cryptography etc. Such data hiding schemes, when coupled with watermarking, can be a part of an extensive layered security approach.

   Audio watermarking is defined as secure communication of data related to the host audio signal, which includes embedding into, and extraction from, the host audio signal [2]. Watermarking, being ideally imperceptible, can be essentially used to mask the very existence of the secret message [3]. In this manner, watermarking is used to create a covert channel to transmit confidential information [4]. Digital audio watermarking involves the concealment of data within a discrete audio file.

2. PROPOSED BLOCK DIAGRAM

   Audio watermarking will be implemented using the following steps

   • Read the secret data to be hidden.

   • Encrypt the secrete data using PN sequence

   • Read the audio signal in which data is to be hidden.

   • Take DFT of the audio signal

   • Hide the data in the audio at the High frequency DFT coefficients.

   • Take IDFT

     The proposed block diagram for audio watermarking is shown in fig 1.

     Audio Output

     Decryption

     Figure 2: Watermarking Extraction

     The extraction of hidden data at the receiver will be implemented using the following steps

   • Read the received Audio

   • Find DFT

   • Extract the watermark from the high frequency DFT coefficients

   • Decrypt the watermark

   1. Audio Input

      MATLAB® audio support provides the ability to:

      • Read and write audio files in common formats such as WAV, AVI, FLAC, MP3, and MPEG-4 AAC

      • Playback and record audio files using the PC sound card

        In this research, an audio file with .wav extension is considered as cover data and sampled at a rate of 44.1 kHz with 16 bit analog to digital converter.

   2. Discrete Fourier Transform [5,6]

      The Fourier transform is simply a method of expressing a function in terms of the sum of its projections onto a set of basis functions. The DFT is a specific kind of discrete transform, used in Fourier analysis. It transforms one function into another, which is called the frequency domain representation, or simply the DFT, of the original function (which is often a function in the time domain). The DFT requires an input function that is discrete.

      The DFT transforms N complex numbers into another sequence of complex numbers, which is defined by F(k) where k=1,1 N-1

      N 1

      F (k) f (n)e

      n0

      • j 2kn N

      (1)

3. IMPLEMENTATION

   The input, i.e., spectrum of the original audio signal from the song Maa Tujhe Salam has been considered as cover audio. The cameraman image from MATLAB directory is

   The inverse Fourier Transform can be calculated using the

   following equation.

   considered as watermark. The cover audio undergoes Discrete Fourier Transform and got converted back into frequency

   domain. The maximum energy components lie near the origin

   F (n)

   1 N 1 F (k)e N n0

   j 2kn N

   (2)

   and low energy components lie at higher frequencies. Hence it has been decided to embed the watermark in the high frequency components of cover audio.

   1. Water mark Selection

      Audio Watermarking can be implemented in 3 ways:

      • Audio in Audio

      • Audio in Image

      • Image in Audio

   2. Need for Encryption[7,8]

   Encryption is the process of transforming data into scrambled unintelligible cipher text using a key. The role of encryption is to secure information when it is stored or in transit. However, it is relatively easier to crack single level encryption by brute force or correlation, as compared to a multi-level encryption scheme. Over the years, Data transmission has been made very simple, fast and accurate using the internet. However, one of the main problems associated with transmission of data over the internet is that it may pose a security threat, i.e., personal or confidential data can be stolen or hacked in many ways. Therefore, it becomes very important to take data security into consideration, as it is

   1. Need for Normalization

      Since the cover signal is the audio signal, the amplitude variations are within ±1V. Watermark is a grey level image with 256 quantization levels where the pixels values lies within the range [0, 255]. Hence it is necessary to normalize the grey values within ±1 V so that without affecting the magnitude spectrum, the stuffing of grey values can be implemented.

   2. Z – Score (ZS) Normalization[11]

      Z-score normalization calculates the normalized scores using arithmetic mean and standard deviation of the given data. This method transforms the score s to a distribution with mean 0 and a deviation of 1. Let mean (s) denote the arithmetic mean of score range and std (s) denote the standard deviation operators, respectively. Then the normalized score

      (N) is calculated as

      N s mean(s)

      one of the essential factors that need attention during the process of data distribution.

      std (s)

      (3)

      Cryptography gained prominence during the Second World War when the allied forces gained an upper hand after they were able to break the German cipher machine, Enigma [9]. These days, it is recognized as one of the major components for providing information security, controlling access to resources and financial transactions. The original data to be transmitted is called plain text which is readable by a person or computer. When it is encrypted, it is known as Cipher Text. The level of security of an encryption algorithm is given by the size of its key space [10]. The larger the key space, the more complicated the encryption algorithm is.

      Cryptography keys are usually classified as symmetric and asymmetric algorithms. In symmetric key algorithms, the sender and receiver use the same keys for encryption as well as decryption. Symmetric key encryption is also known as secret key as both, the sender and receiver have to keep the key protected [7,10]. The level of security entirelydepends on how well the sender and the receiver keep the key protected. If the unauthorized person is able to get the key, he can easily decipher the encryption using it. This is the major limitation of the symmetric key algorithm.

      In symmetric key encryption, a secure mechanism is required to deliver keys properly while asymmetric keys result in better key distribution. Symmetric key provides confidentiality but not authenticity as the secret key is shared [8]. Asymmetric keys on the other hand, provide both authentication and confidentiality [7, 8].

      After normalization, the high frequency components of cover audio are replaced with normalized values of watermark pixel grey values. After completing the process, IDFT is taken for the combined data so that the signal comes back to time domain. This signal almost resembles the original signal as shown in fig.3.

      For the extraction of the watermark, the DFT of the received audio has been taken and from the high frequency components, the watermark gets extracted as shown in fig.3

   3. PN sequence based Encryption[13]

      PN sequences look like random noise but they are not purely random in nature. When we multiply the PN sequence with an image, the resulting image resembles noise. However, during decryption, this noise-like signal can be used to exactly reconstruct the original image by multiplying it by the same pseudorandom sequence. Using this scheme, the initial seed state which is nothing but the key is only needed to generate exactly the same sequence of length. We have decided the following algorithm to generate the seed state for the PN sequence generator.

      • Decide the password.

      • Generate the seed state from the password

        Since the seed state is known only to the authorized user, an intruder trying to access the database will not be able to decrypt the templates.

        Figure 3. Watermark Extraction

        The encryption scheme using PN sequence is done as given below [13]:

      • Let the watermark be image K

      • Generate the Pseudo image R using the seed state. The size of R image should be same as image K

      • Do the bit Ex-OR operations on both the images and find out the resultant image C as

        C= bit XOR(R, K) (4)

        The decryption scheme using PN sequence is done with the help of following steps [13]:

      • Generate the Pseudo image R using the seed state. The size of R image should be same as extracted image C

      • Do the bit Ex-OR operations on both the images R and C

      • The watermark is extracted as

   W= bit XOR(R, C) (5)

   Figure 4: Encrypted Watermark extraction

4. RESULT ANALYSIS

   For all practical purposes, it is preferable to have a quantitative measurement to provide an objective judgment of the extracting fidelity. This is done by calculating the following parameters in each case. Results, thus obtained, have been tabulated. For performance evaluation, an audio clipping with one minute duration is considered as cover data.

   1. Mean Square Error

      Mean Square Error (MSE) [14], first introduced by C. F. Gauss, serves as an important parameter in gauging the performance of the watermarking system. MSE is essentially a signal fidelity measure [15, 16]. The goal of a signal fidelity measure is to compare two signals by providing a quantitative score that describes the degree of similarity/fidelity. Suppose that

      x = {xi | i = 1, 2, N} and

      y = {yi| i = 1, 2, N} are two finite-lengths, discrete signals, The MSE between the signals are given by the following formula:

      The watermarked image is encrypted using PN sequence and the resultant encrypted image is transmitted to the receiver along with cover audio. At the receiver side, first the encrypted watermark is extracted from the cover audio and then

      MSE

      1 ( X

      N

      i

      N i1

      Y )2

      (6)

      i

      decryption using PN sequence is done on the extracted image as shown in fig.4

      where, N is the number of signal samples, xi is the value of the

      ith sample in x. yi is the value of the ith sample in y.

   2. Entropy

      The entropy H of a discrete random variable X is given as

      H (x) E(I (x))

      (7)

      Here E is the expected value, and I is the information content of X. If p denotes the probability mass function of X then the entropy can be explicitly written as:

      n n

      H (x) p(xi )I (xi ) p(xi ) log2 p(xi )

      i1

      i1

      (8)

   3. Moment

   In image processing, computer vision and related fields, an image moment is a certain particular weighted average (moment) of the image pixels intensities, or a function of such moments, usually chosen to have some attractive property or interpretation. For a gray scale image with pixel intensities I(x, y), raw image moments are calculated by:

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      Table I. Performance Evaluation (1minute audio)

      Parameter	Simple Watermark	Encrypted watermark
      Entropy	7	7
      Moment	1.17 109	1.17 109
      MSE	0	0
      Embedding Time	1.16s	1.49s
      Extraction Time	0.18s	0.77s
      MSE in Audio	2.2 104	5.11104

5. CONCLUSION

In this paper, an audio watermarking scheme based on Discrete Fourier Transform has been implemented. To enhance the security of the system, A PN sequence based encryption has also been considered. The proposed algorithms were implemented using MATLAB 2010a on an Intel core i3 based platform. For a cover audio of size one minute, the average time taken for extraction simple watermarking with an image of size 256 × 256 was 0.18 seconds and 0.77 seconds using the PN sequence scheme. In terms of performance, the calculated values of MSE, entropy and moment are found to be the same for both the schemes. Since the MSE between original audio and watermarked audio was very high, the clarity of the audio at the receiver decreased. As a future scope, this MSE can be reduced with the help of some other watermarking techniques.

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