Enhanced Audio Cryptosystem Using Multiple Secret ...

Minufiya J. of Electronic Engineering Research (MJEER), Vol. 25, No. 2, July 2016.

Enhanced Audio Cryptosystem Using Multiple Secret Keys and Permutations in Time and Transform Domains Mahmoud Farouk1, Osama S. Faragallah1,2 , Osama A. Elshakankiry1,2, Ahmed Elmhalaway1, and Hala S. El-sayed3 1

Dept. of Computer Science and Eng., Faculty of Elect., Eng., Menoufia University Dept. of Information Technology, College of Computers and Information Technology, Taif University, Al-Hawiya 21974, Kingdom of Saudi Arabia. 3 Dept. of Electrical Engineering, Faculty of Engineering, Menoufia University. 2

(Received: 13-December-2015 – Accepted: 04-April-2016)

Abstract This paper presents an enhanced audio speech cryptosystem based on three enhancements for permutation and substitution of speech segments using multiple secret keys and permutations in time and transform domains. The first enhancement method is based on repeating the first and last permutation steps in both encryption/decryption procedures for several times to enhance the security level. The second and third enhancement methods are based on adding two additional secret keys generated from the original key besides the utilized three keys. This adds another security layer for the modified developed audio cryptosystem. Simulation experiments demonstrated that these modifications enhance the security level and quality for the audio cryptosystem compared with a recent audio cryptosystem.

1. Introduction Audio speech communications hold secrets range from personal information to national security information. There is an urgent need to secure speech information during transmission through insecure channels. Cryptographic techniques are utilized to convert speech from intelligible form to unintelligible form before transmission. ____________________________________________________________ 191


Symmetric key and public key algorithms cause large delays in real time applications [1, 2, 3]. On the other hand, chaotic maps have several advantages because of their random-like behavior, their sensitivity to initial conditions, and their high confusion properties [4]. Therefore, chaotic maps are widely used in image processing and audio speech processing. This research paper presents three enhancements for a recent audio speech cryptosystem introduced by E. Mosa et al. [5] to enhance the security and quality level by adding two more keys in addition to add a new security layer through repeating permutation in encryption/decryption algorithms. These enhancements are at the cost of an increase in average time taken to execute the algorithms in time domain only, while it shows enhancement in transform domains. The proposed audio cryptosystem is compared to a recent audio speech cryptosystem [5] and demonstrated enhancements in security, quality level and average time taken to execute encryption/decryption algorithms. The rest sections of the paper are organized as the follows. Section 2 discusses the Advanced Encryption Standard (AES). Section 3 discusses the baker map. Section 4 discusses Discrete Transform Domains. Section 5 discusses The Audio Cryptosystem [5]. Section 6 presents the proposed enhanced audio cryptosystem. Section 7 discusses Quality Metrics for Audio Speech Cryptosystem. Section 8 discusses simulation results. Finally, section 9 concludes the paper.

2. Advanced Encryption Standard (AES) AES is a specification for the encryption of electronic data, established by the U.S. National Institute of Standards and Technology (NIST) in 2001. It has been adopted by the U.S. government and is now used worldwide. It is regarded as one of the best secure algorithms used in symmetric key cryptography [6]. The AES has disadvantages regarding its sensitivity to noise due to its high diffusion, while it keeps advantages as a very strong and secure algorithm. It is based on combination of both substitution and permutation. It is fast in both software and hardware implementations. It uses a fixed block size of 128 bits and number of key sizes of 128, 192 or 256 bits. Number of rounds depends on key size which is 10, 12, or 14 rounds respectively. Each round except the first and last rounds consists of a fixed sequence of transformations named Sub-Byte, The Shift-Row, the Mix-Column and Add-Round-Key [7]. ____________________________________________________________ 191


3. Baker Map Baker map is a two dimensional (2D) chaotic map. It is used in permutation of speech information to provide some kind of security and hiding original audio speech's information. It is achieved by using a secret key to rearrange elements in a square matrix in new positions [8-9]. It retains the advantages of chaotic systems such as non-predictability, good randomness and low correlation [10-13]. There exist two kinds of chaotic baker map; generalized and discretized map [14-15]. This paper utilizes discretized baker map as it is being used by a recent developed audio cryptosystem [5] and has an advantage that it is an effective method to randomize the elements in a square matrix. This type of chaotic baker map rearranges an element in a square matrix to another position in the same matrix [16]. Discretized Baker map is denoted by DBM(x1,x2,……,xk), values of n integers ( x1,x2,……,xk), is selected so that each integer xi divides M, and Mi = x1 + … +xi. an item at the position (l, k), adheres with conditions such that 0 ≤ k ≤ M and Mi ≤ l ≤ Mi +xi, is mapped to the new position by the following equation [15]: M  M  u   M  DBM ( x1 ,...,xk ) (l , k )   (l  N i )  l mod , i  k  k mod    M i   xi   xi  M   xi    

(1)

The following conditions are applied to equation 1: (a) M builds an M×M matrix of x vertical rectangles with height M and width xi. (b)Each vertical rectangle contains xi boxes, and each box contains M elements. (c) Mapping each box to a row of elements, and a column by column while right box at top and left box at bottom.

4. Discrete Transform Domains 4.1 Discrete Cosine Transform The DCT of a data sequence x(n), n = 0, 1,…,(N - 1) is defined as [23-27]:

____________________________________________________________ 191

Minufiya J. of Electronic Engineering Research (MJEER), Vol. 25, No. 2, July 2016. N 1   (2n  1)k  X (k )   (k ) x(n) cos  2N   n 0

, k  0,1,2......, N - 1

(2)

Where,

(0) 

1 N

(k ) 

,

2 N

(3)

4.2 Discrete Sine Transform The DST of a data sequence x(n), n = 1, 2,…,N is defined as [26-28]: N 1    X (k )   x(n) sin (n  1)(k  1)  , k=0, …….., N-1  N 1  n 0

(4)

4.3 Discrete Wavelet Transform Wavelet transform is a mathematical operation used to divide a given audio signal into different sub-bands of different scales to study each scale separately. The Haar wavelet is the simplest type of wavelets. The Haar transform serves as a prototype for all other wavelet transforms [29]. It uses the simplest possible Pt (Z ) with a single zero at Z  1 . It is represented as follows [30]:

Pt ( Z)  1  Z and



Z 



1 z  z 1 2





1 z  2  z 1 2 1  z  1 1  z 1  G 0 z H 0 z  2

P( z) 

(5)





(6)

H 0 z  and G 0 z  can be written as follows:

____________________________________________________________ 191


H 0 z  



1 1  z 1 2



(7)

G 0 z   z  1

(8)

With k=1 G 1 ( z )  zH 0  z  





1 1 z 1  z 1  z  1 2 2





H1 (z)  z 1G 0  z  z 1  z  1  z 1  1

(9) (10)

The two outputs of H 0 ( z ) and H1 (z) are concatenated to form a single vector of the same length as the original speech signal.

5. The Audio Cryptosystem The Audio Cryptosystem shown in Fig. 1(a) consists of the following main steps [5]: 1) Keys generation: The first key shared between sender and receiver can be generated by a pseudo random generator. It is used to generate the other two keys, key 2 is an inverse of original key and key 3 is an inverse of the two halves of key1. These three keys are used in both encryption/decryption algorithms. 2) Mask Generation: It is very necessary step in order to change the remaining non-permutated portions of speech signal and to increase the security of the audio cryptosystem. The utilized mask is generated from the key using a number of circular shifts of the key equal to the number of sample rows minus one. 3) Block randomization: It depends on the idea of AES for circular shifts. The first row remains unchanged, while the second row is shifted right a single step, and third row is shifted two steps to right and so do for the other rows. The reverse operation is applied with the decryption algorithm. 4) Permutation with Keys: It is utilized to change the characteristics of the speech signal and it is based on chaotic baker map. ____________________________________________________________ 191


5) Substitution: This step used to change the remaining non-permutated portions of speech and to increase the security of the audio cryptosystem. We add mask in encryption and subtract mask from original speech signal in decryption algorithm. 6) Discrete Transforms: The main idea of using the DCT, DST, or DWT [17-19] is to increase diffusion and change the values of ones introduced by masking in silent periods (all-zero blocks). Each of these transforms has a strong diffusion mechanism. All samples in the time domain contribute to each sample in the transform domain, which guarantees a totally different shape of the transformed signals. So another permutation step is performed on the transform-domain samples to increase the security prior to the inversion of this transform and the application of another permutation step in time domain. 7) Permutation with keys and Substitution: These two steps are performed on the transform-domain samples to increase the security prior to the inversion of this transform and the application of another permutation step in time domain. 8) Inverse Discrete Transforms: An inverse transform is perfumed. 9) Last Permutation with keys: A final permutation step is then performed to complete the hiding of the audio signal features.

6. The Proposed Enhanced Audio Cryptosystem The proposed audio cryptosystem is a modification for a recent work on audio speech cryptosystem presented by E. Mosa et al [5]. The aim of modification is to get a secure and faster audio cryptosystem. The recent work on audio speech cryptosystem introduced by E. Mosa et al [5] is shown in Fig. 1(a) while the proposed enhanced audio cryptosystem is shown in Fig. 1(b). The audio cryptosystem in [5] considers results based on TD, DCT and DST transform domains given in [30-31], The results based on DWT [32] was not included and it will be discussed here. There are three modifications added to the audio cryptosystem [5] to produce the proposed enhanced audio cryptosystem.

____________________________________________________________ 191


(a)

(b)

Fig. 1: (a) The Audio Cryptosystem in [5], (b) The Proposed Enhanced Audio Cryptosystem

1. The first modification is based on adding a loop in encryption/decryption algorithms. The loop is added to repeat the first and last permutation steps in encryption/decryption procedures presented in steps 4, 9 in the audio cryptosystem [5]. The number of repetition for this loop is set to minimum possible applicable number of sub-keys which is three. This modification will add additional layer of security to the proposed enhanced audio cryptosystem. 2. The second modification is based on increasing the number of secret keys utilized to four keys instead of three keys and use a third mask, presented in steps 1, 2 in the audio cryptosystem [5], respectively. ____________________________________________________________ 191


The key 4 equals to inverse bits for key 3. Also, the mask 3 is generated to be handled with the operations of key 3. 3. The third modification is done by using a fifth key (key 5) and using a fourth mask, presented in steps 1, 2 in the audio cryptosystem [5] respectively. The key 5 is produced by inversing the two halves of key 2. Also, the mask 4 is generated to be handle with operations of key4.

7. Quality Metric Parameters for Audio Speech Cryptosystem 7.1 Correlation Coefficient It is an important metric used to measure correlation coefficient (CC) between similar samples in original and encrypted signals to determine the encryption quality of audio cryptosystem. Lower values for CC indicates better encryption quality. It is defined as follows [17]: rxy =

c v ( x, y ) D( x) D( y )

(11)

Where, cv (x,y) is the covariance between the original signal s and the encrypted signal y. D (x) and D ( y ) are the variances of the signals x and y, respectively. In numerical calculations, the following discrete formulas can be applied [17]:

E ( x) 

D( x ) 

1 Nx

Nx

 x ( n)

1 Nx ( x(n)  E (n)) 2  Nx n 1

c v ( x, y ) 

(12)

n 1

1 Nx

(13)

Nx

 ( x(n)  E ( x))( y(n)  E ( y))

(14)

n 1

Where, Nx is the number of voice samples involved in the computations. ____________________________________________________________ 191


7.2 Spectral Distortion (SD) The SD is a metric that is applied in frequency domain on the frequency spectra of the original and processed encrypted voice signals. It is computed in dB to give how far is the spectrum of the processed encrypted voice signal from that of the original voice signal. The higher the SD between the original and encrypted voice signals, the better is the encryption quality. The SD can be computed as follows [18-20]:

SD 

1 M

M 1 Ls m  Ls 1

 

m  0 n  Ls m

Vs (k )  Vy (k )

(15)

Where, Vs (k ) is the spectrum of the original voice signal in dB for a certain segment, Vy (k ) is the spectrum of the processed encrypted voice signal in dB for the same segment, M is the number of segments and Ls is the segment length.

7.3 Log Likelihood Ratio (LLR) The LLR metric for an audio signal is based on the assumption that each segment can be represented by an all-pole linear predictive coding model of the form [21, 22]: mp

s(n)   am s(n  m)  Gsu(n)

(16)

m 1

Where, am (for m=1, 2, ….., mp) are the coefficients of the all-pole filter, Gs is the gain of the filter and u (n) is an appropriate excitation source for the filter. The audio signal is windowed to form frames of 15 to 30 ms length. The LLR metric is then defined as [21]:

   as R y asT  LLR  log   T  a R a  y y y 

(17)

 Where, a s is the LPCs coefficient vector [1, as (1), as (2), . . ., as (mp)] for  the original clear audio signal, a y is the LPCs coefficient vector [1, a y (1),

____________________________________________________________ 199


a y (2), . . . , a y (mp)] for the decrypted audio signal, and R y is the autocorrelation matrix of the decrypted audio signal. The closer the LLR to zero, the higher is the quality of the decrypted audio signal.

7.4 Average Processing Time The average time to execute encryption/decryption algorithms is called as average time (Avg. Time) and measured in seconds. It is computed using: Avg. Time = (Time taken to execute Encryption + Time taken to execute Decryption)/2 (18)

8. Simulation Results The hardware specification used in this simulation is a HP Pavilion g series laptop with intel® Core ™ i3 CPU M370 2.40 GHz processor and 4GB RAM. The Software used for simulation is MATLab 7.10.1 (R 2010a) and the operating system is windows 7 ultimate. Audio sample used is an artificial speech signal consists of four parts, as shown in Fig. 2 and Table1. Table 1: Specifications of Audio Speech Sample

Duration in Sec First Part Second Part Third Part

2.5 1.5 1.5

Fourth Part

2.5

Content

sentence "we were away years ago" Ideal silence without noise

Non-perfect silence with noise sentence "we were away years ago"

Speaker's Sex Female Silence Silence Male

The first and last parts are of length of 2.5 seconds, a female says the sentence "we were away years ago". A male says the same sentence in the fourth part. The third part consists of ideal silence without noise of 1.5 seconds length and followed by part three of length 1.5 seconds of nonperfect silence with noise. ____________________________________________________________ 122


(a)

(b)

(c) Fig. 2: (a) Original speech signal, (b) Spectogram of Original Signal, (c) Histogram of original Signal

____________________________________________________________ 121


8.1 TD Analysis The audio cryptosystem in [5] and the proposed enhanced audio cryptosystem are applied on time domain. They are applied on the same audio speech signal and on the same testing conditions. The analysis and comparison between the two audio cryptosystems are given in the following subsections. Table 2: TD Analysis The Audio Cryptosystem [5]

Decryption

Encryption

TD

The Proposed Enhanced Enhancement Audio % Cryptosystem

CC

0.0039

0.0027

30.8%

SD

25.8313

24.0931

-6.7%

LLR

0.637

0.5014

-21.3%

CC

0.9993

0.9993

0.0%

SD

0.0448

0.0448

0.0%

LLR

1.37E-07

1.51E-07

10.7%

Avg. Time(seconds)

0.0515

0.127

-146.6%

Enhancement percentage values shown in last column in Table 2 are calculated as the following: Enhancement % = [(Value in The Audio Cryptosystem in [5] – Value in The Proposed Enhanced Audio Cryptosystem) / Value in The Audio Cryptosystem in [5]] * 100 (19)

For Encryption results shown in Table 2, it is noticeable that value of Correlation Coefficient (CC)’s for the proposed enhanced audio cryptosystem is enhanced by 30.8% compared with audio cryptosystem in [5]. The histogram of Fig. 3(c) for the proposed enhanced audio cryptosystem is more uniform than the histogram of Fig. 3(d) for the audio cryptosystem in [5]. This means that the proposed enhanced audio cryptosystem enhances security. ____________________________________________________________ 121


(a)

(b)

(c)

(d)

Fig. 3: TD Results (a) Original and encrypted audio signal using the Enhanced Audio Cryptosystem, (b) Spectogram of original and encrypted audio signal using the Enhanced Audio Cryptosystem (c) Histogram of Original and encrypted audio signal using the Enhanced Audio Cryptosystem, (d) Histogram of Original and encrypted audio signal using the The Audio Cryptosystem in [5]

For decryption, the proposed enhanced audio cryptosystem shows an enhancement for value of LLR as compared to the value using the audio cryptosystem in [5] by 10.7%, while values for CC and SD still the same. For average time's comparison, there is an increase in average time for the proposed enhanced audio cryptosystem. This is expected because of repeated permutations, it increases by 146.60% than the audio ____________________________________________________________ 121


cryptosystem in [5], such the percentage of increase must be considered when deciding to use TD with proposed cryptosystem and must be compared with benefits gained in both security and quality's levels. The proposed enhanced audio cryptosystem provides better security and quality compared with the audio cryptosystem in [5] represented by better values showed for both correlation Coefficient (CC) in encryption algorithm and Log Likelihood Ratio (LLR) in decryption algorithm. Also it provides an added layer of security by adding two more keys (fourth and fifth keys) to the cryptosystem.

8.2 DCT Analysis The audio cryptosystem in [5] and the proposed enhanced audio cryptosystem are applied on Discrete Cosine Transform (DCT). They are applied on the same audio speech signal and using the same testing conditions. Table 3: DCT Analysis The Audio Cryptosystem [5]

Decryption

Encryption

DCT


CC

9.55E-04

-4.09E-05

104.3%

SD

13.9707

13.8795

-0.7%

LLR

0.4226

0.4491

6.3%

CC

0.9803

0.9775

-0.3%

SD

1.1876

1.384

-16.5%

LLR

0.012

0.0162

-35.0%

Avg. Time(seconds)

0.099

0.0885

10.6%

Table 3 shows that there is an enhancement for encryption phase of the proposed enhanced audio cryptosystem with respect to Correlation Coefficient (CC) than that for the audio cryptosystem in [5] by 104.3%, ____________________________________________________________ 121


and Log Likelihood Ratio (LLR) by 6.3%. Also, the histogram of the proposed enhanced audio cryptosystem shown in Fig. 4(c) is more uniform than histogram of the audio cryptosystem in [5] shown in Fig. 4(d). Finally, the average time for the proposed enhanced audio cryptosystem is better than that for the audio cryptosystem in [5] by 10.6%.

(a)

(b)

(c)

(d)

Fig. 4: DCT Results (a) Original and encrypted audio signal using the Enhanced Audio Cryptosystem, (b) Spectogram of original and encrypted audio signal using the Enhanced Audio Cryptosystem (c) Histogram of Original and encrypted audio signal using the Enhanced Audio Cryptosystem, (d) Histogram of Original and encrypted audio signal using the The Audio Cryptosystem in [5]

____________________________________________________________ 121


The proposed enhanced audio cryptosystem enhances security level with Discrete Cosine Transform (DCT) for encryption phase only. Also the proposed enhanced audio cryptosystem provides a benefit of using two more secret keys which provides another layer of security by using five secret keys instead of three secret keys.

8.3 DST Analysis The audio cryptosystem in [5] and the proposed enhanced audio cryptosystem are applied on Discrete Sine Transform (DST). They are applied on the same audio speech signal and with the same testing conditions. Table 4: DST Analysis The Audio Cryptosystem [5]

Decryption

Encryption

DST


CC

0.0011

-0.0013

218.2%

SD

13.9045

13.93

0.2%

LLR

0.455

0.4082

-10.3%

CC

0.9803

0.9726

-0.8%

SD

1.2067

1.5818

-31.0%

LLR

0.0112

0.0163

-45.5%

Avg. Time (seconds)

0.088

0.0895

1.7%

For encryption's results shown in Table 4, it is clear that the values of Correlation Coefficient (CC)’s and Spectral Distortion for the proposed enhanced audio cryptosystem are enhanced by 218.2% and 0.2%, respectively. Also the histogram of the proposed enhanced audio cryptosystem shown in Fig. 5(c) is more uniform than the histogram of the audio cryptosystem in [5] shown in Fig. 5(d). For decryption resuls, there is no enhancement shown, while the average time is enhanced compared with the audio cryptosystem in [5] by 1.7%. ____________________________________________________________ 121


(a)

(b)

(c)

d)

Fig. 5: DST Results (a) Original and encrypted audio signal using the Enhanced Audio Cryptosystem, (b) Spectogram of original and encrypted audio signal using the Enhanced Audio Cryptosystem (c) Histogram of Original and encrypted audio signal using the Enhanced Audio Cryptosystem, (d) Histogram of Original and encrypted audio signal using the The Audio Cryptosystem in [5]

In summary, there is an enhancement for both security and quality levels for the proposed enhanced audio cryptosystem in Discrete Sine Transform (DST) for encryption phase only. ____________________________________________________________ 121


8.4 DWT Analysis The audio cryptosystem in [5] and the proposed enhanced audio cryptosystem are applied on Discrete Wavelet Transform (DWT). They are applied on the same audio speech signal and on the same testing conditions. Table 5: DWT Analysis

The Audio Cryptosystem [5]

Decryption

Encryption

DWT


CC

-0.0033

0.0028

-184.8%

SD

15.6169

15.4779

-0.9%

LLR

0.4155

0.5301

27.6%

CC

0.9761

0.9789

0.3%

SD

1.3911

1.3104

5.8%

LLR

1.69E-02

1.13E-02

33.1%

Avg. Time(seconds)

0.1445

0.1025

29.1%

For Encryption results shown in Table 5, it is noticeable that the Log Likelihood Ratio (LLR)’s for the proposed enhanced audio cryptosystem is enhanced by 27.6% compared with audio cryptosystem in [5]. For Decryption results shown in Table 5, it is noticeable Correlation Coefficient (CC), Spectral Distortion (SD) and Log Likelihood Ratio (LLR)’s for the proposed enhanced audio cryptosystem are enhanced by 0.3%, 5.8% and 33.1% respectively, compared with audio cryptosystem in [5]. The average time of the proposed enhanced audio cryptosystem is enhanced by 29.1% compared with audio cryptosystem in [5]. There is an enhancement in security, quality and average time’s levels for the proposed enhanced audio cryptosystem for Discrete Wavelet Transform (DWT) for encryption/decryption phases. At the same time, there is another benefit by adding a new layer of security by using five keys instead of three keys only. ____________________________________________________________ 121


9. Conclusion The proposed enhanced audio cryptosystem enhances security, quality levels processing speed compared with a recent audio cryptosystem. With respect to TD, the proposed enhanced audio cryptosystem enhances the security and quality levels, and also proves its applicability for use in real time. With respect to DCT, the proposed enhanced audio cryptosystem shows an enhancement in encryption phase. With respect to DST, the proposed enhanced audio cryptosystem enhances only the encryption phase and enhance the avergae time. With respect to DWT, the proposed enhanced audio cryptosystem enhances encyrption/deceryption phases and average time.

References [1]

[2] [3]

[4] [5]

[6] [7] [8]

[9]

National Bureau of Standards, "Data Encryption Standard", Federal Information Processing Standards Publication no.46, U.S. Government Printing Office, Washington, DC (1977). Anoop, "Public Key Cryptography-Applications algorithm and mathematical explanations", 2007. T. Thongpon and K. Sinchai, "Accelerating Asymmetric-key Cryptography using Parallel-key Cryptographic Algorithm (PCA)", 6th International Conference on computer and Information Technology, vol. 2, pp. 812 – 815, 2009. J. Fridrich, "Symmetric Ciphers Based on Two-dimensional Chaotic Maps," Int.J. Bifurcation and Chaos, vol. 8, no. 6, pp. 1259–1284, 1998. Mosa, E., et al. "Encryption of speech signal with multiple secret keys in time and transform domains." International Journal of Speech Technology 13.4 (2010): 231-242. "Advanced Encryption System", Federal Information Processing Standards Publication 197, 2001. J. Daemen and V. R. Rijndael, "The advanced encryption standard", Dr. Dobb’s J. vol. 26, no. 3, pp. 137–139, 2001. Fu, Chong, et al. "An efficient and secure medical image protection scheme based on chaotic maps." Computers in biology and medicine 43.8 (2013): 1000-1010. Elshamy, Ahmed M., et al. "Optical image encryption based on chaotic baker map and double random phase encoding." Journal of Lightwave Technology 31.15 (2013): 2533-2539.

____________________________________________________________ 129


[10] [11]

[12]

[13]

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

Elgazar, Safaa, et al. "Efficient Watermarking of Audio Signals." Digital Signal Processing 5.12 (2013): 412. Elshamy, Ahmed M., et al. "Optical image encryption based on chaotic baker map and double random phase encoding." Journal of Lightwave Technology 31.15 (2013): 2533-2539. Y. Zhai, S. Lin and Q. Zhang, "Improving Image Encryption Using Multi-Chaotic Map", Workshop on Power Electronics and Intelligent Transportation System, 2008. PEITS '08, pp. 143 – 148, 2-3 August, 2008. Nassar, S. S., et al. "Multi-level Security Technique Using Steganography with Chaotic Encryption." Digital Image Processing 6.3 (2014): 142-148. Chen, Jun-Xin, et al. "Cryptanalysis and improvement of an optical image encryption scheme using a chaotic Baker map and double random phase encoding." Journal of Optics 16.12 (2014): 125403. Wang, Xingyuan, and Dapeng Luan. "A novel image encryption algorithm using chaos and reversible cellular automata." Communications in Nonlinear Science and Numerical Simulation 18.11 (2013): 3075-3085. Elhoseny, Heba M., et al. "Chaotic encryption of images in the fractional Fourier transform domain using different modes of operation." Signal, Image and Video Processing (2013): 1-12. G. Manjunath and G. V. Anand, "Speech encryption using circulant transformations, " Proc. IEEE, Int. Conf. Multimedia and Exp, Vol. 1, pp. 553-556, August, 2002. P. Hedelin, F. Norden, F and J. Skoglund, "SD optimization of spectral coders", IEEE Workshop on Speech Coding Proceedings, pp. 28 – 30, 1999. S. Wang, A. Sekey, A. Gersho, "An objective measure for predicting subjective quality of speech coders", IEEE Journal on selected areas in communication, Vol. 10, No. 5, pp. 819-829, June 1992. W. Yang, M. Benbouchta, R. Yantorno, "Performance of the modified bark spectral distortion as an objective speech quality measure", Proc. IEEE International Conference on Acoustic, Speech and Signal Processing, Vol. 1, Washington, USA , pp. 541-544. May 1998. W. K. Sang, G. K. Young and M. K. Simon, "Generalized selection combining based on the log-likelihood ratio", IEEE International Conference on Communications ICC 2003, vol. 4, pp. 2789-2794, 2003. J. K. Kwon, S. Park, D. K. Sung, "Collision mitigation by loglikelihood ratio (LLR) conversion in orthogonal code-hopping

____________________________________________________________ 112


[23]

[24]

[25]

[26]

[27]

[28]

[29] [30]

[31] [32]

multiplexing", IEEE Transactions on Vehicular Technology, vol. 55, issues 2, pp. 709-717, March 2006. L. Meng-En, C. Chien-Feng, L. Tsung-Nan and C. Chun-Nan, "The application of discrete cosine transform (DCT) combined with the nonlinear regression routine on optical auto-focusing", Digest of Technical Papers International Conference on Consumer Electronics ICCE 2009, pp. 1 – 2, 2009. L. Z. Cheng, "On computing the two-dimensional (2-D) type IV discrete cosine transform (2-D DCT-IV)", IEEE Signal Processing Letters, vol. 8, Issue. 8, pp. 239 – 241, August 2001. N. Ahmed, N, T. Natarajan and K. R. Rao, "Discrete Cosine Transfom", IEEE Transactions on Computers, vol. C-23 , no.1, pp. 90 – 93, June 1974. N. R. Murthy and M. N. Swamy, "Efficient algorithms for the computation of running discrete cosine and sine transforms", IEEE International Symposium on Circuits and Systems, vol. 1, pp. 634 - 637, 11-14 June, 1991. P. Yip and K. Rao "On the shift property of DCT's and DST's" Acoustics, Speech and Signal Processing, vol. 35, Issue. 3, pp. 404 – 406, March 1987. L. Xueyao X. Hua and C. Bailing, "Noisy Speech Enhancement Based on Discrete Sine Transform", First International Multi-Symposiums on Computer and Computational Sciences, IMSCCS 2006, vol. 1, pp. 199 – 202, 2006. A. Prochazka, J. Uhlir, P. J. W. Rayner and N. J. Kingsbury, Signal Analysis and Prediction. Birkhauser Inc, 1998. B. Goldburg, S. Sridharan and E. Dawson, "Design and cryptanalysis of transform-based analog speech scramblers", IEEE J. Select. Areas Communication, vol. 11, no. 5, pp. 735-744, 1993. Rao, K. Ramamohan, and Ping Yip. Discrete cosine transform: algorithms, advantages, applications. Academic press, 2014. F. Ma, J. Cheng, and Y. Wang, "Wavelet transform-based analogue speech scrambling scheme", Electronics Letters, vol. 32, no. 8, pp. 719721, 1996.

____________________________________________________________ 111

‫‪Minufiya J. of Electronic Engineering Research (MJEER), Vol. 25, No. 2, July 2016.‬‬

‫ملخص‬ ‫انولّ انبذثيّ تمذو َظاو تشفيش صوتي يتطوس ويذسٍ ‪ ,‬إعتًادا عهي ثالثّ‬ ‫تذسيُات أضيفت عهي َظاَى تشفيش صوتي سبك تمذيًّ وعهي تطبيك انتذسيُات‬ ‫عهي طشق انتبذيم واإلستبذال انًستخذيّ ‪ ,‬وبإستخذاو أكثش يٍ يفتاح سشى في‬ ‫انًجال انزيُي وانًجاالت انتذويهيّ‪ .‬انتطويش األول يعتًذ عهي تكشاس عًهيّ‬ ‫انتبذيم األوني واألخيشِ في انًُظويّ عذد يٍ انًشات ورنك نتذسيٍ يستوى‬ ‫األياٌ‪ ,‬انتطويش انثاَي وانثانث يعتًذ عهي إضافّ يفتادي أياٌ يتونذيٍ يٍ‬ ‫انًفتاح انسشى األصهي‪ ,‬وبزنك يكوٌ نذيُا خًس يفاتيخ سشيّ بذال يٍ ثالثّ‬ ‫يفاتيخ فمظ‪ .‬وْزا يضيف دسجّ أخشى يٍ انسشيّ واألياٌ نُظاو انتشفيش انصوتي‬ ‫‪ .‬اإلختباسات أثبتت أٌ ْزِ انتطويشات انثالثّ دسُت دسجّ األياٌ وانسشيّ‬ ‫وانجودِ نُظاو انتشفيش انصوتي انسابك‬

‫____________________________________________________________‬ ‫‪111‬‬