Reconfigurable Secure Video Codec Based on DWT ...

Journal of Applied Computer Science & Mathematics, no. 9 (4) /2010, Suceava

Reconfigurable Secure Video Codec Based on DWT and AES Processor D. DIA, M. ZEGHID, 1M. ATRI, B. BOUALLEGUE, M. MACHHOUT and R. TOURKI Electronic and Micro-Electronic Laboratory, Faculty of Sciences Monastir, 5000, Tunisia 1 [email protected] Abstract-In this paper, we proposed a secure video codec based on the discrete wavelet transformation (DWT) and the Advanced Encryption Standard (AES) processor. Either, use of video coding with DWT or encryption using AES is well known. However, linking these two designs to achieve secure video coding is leading. The contributions of our work are as follows. First, a new method for image and video compression is proposed. This codec is a synthesis of JPEG and JPEG2000, which is implemented using Huffman coding to the JPEG and DWT to the JPEG2000. Furthermore, an improved motion estimation algorithm is proposed. Second, the encryptiondecryption effects are achieved by the AES processor. AES is aim to encrypt group of LL bands. The prominent feature of this method is an encryption of LL bands by AES-128 (128-bit keys), or AES-192 (192-bit keys), or AES-256 (256-bit keys). Third, we focus on a method that implements partial encryption of LL bands. Our approach provides considerable levels of security (key size, partial encryption, mode encryption), and has very limited adverse impact on the compression efficiency. The proposed codec can provide up to 9 cipher schemes within a reasonable software cost. Latency, correlation, PSNR and compression rate results are analyzed and shown.

I. INTRODUCTION The past decade, several efforts [1, 2, 3] have developed video encryption schemes for secure transfer of multimedia information. Many of these approaches exploit the inherent characteristics of video data and the steps taken to compress and encode video information. The typical steps performed in video compression include motion estimation, DCT or DWT transform, quantization, and entropy encoding. These techniques can be classified into three types according to the target data selected from compression stages. The first one is named “Spatial Domain” schemes, which apply encryption to the original video data directly as in [1]. The second scheme is named “Bitstream Domain”, which encrypts the code words of compressed bitstream. In [2] “Naive Algorithm” was proposed as an example of “Bitstream Domain”. To reduce the amount of processing overhead, the third schemes known as “Frequency Domain” has been proposed, which use the results of DCT or DWT transformation and quantization stages from compression process. These schemes selectively encrypt the motion estimation process or DCT/DWT data result. So that they belong to the selective encryption. In [3, 4], Shi and Bhargava propose some methods, which encrypt the sign bits of DC and AC coefficients of every block and

that of all the motion vectors. Tang proposed another “Frequency Domain” selective encryption scheme, which is called “Block Shuffle” [5]. “Subband Shuffle” [6] is another “Frequency Domain” technique belongs to a joint encryption and compression framework. The main method described in this framework is a shuffling of the DCT coefficients within a subband. From this point of view the DWT is a very attractive version of the frequency models for the Human Visual System (HVS) [7]. Huffman coding presents an advantage to be less complex and contains few calculations. Therefore, we have synthesized our codec by synthesis of the JPEG2000 and JPEG added to a motion estimation block. Our codec present the block DWT of JPEG2000 and the block Huffman coding of JPEG. We improved an approach called band LL encryption to reduce encryption and decryption time in video communication and processing. The encryption-decryption effects are achieved by the AES algorithm [8]. The rest of this paper is organized as follows. Section 2 introduces our proposed secure video codec scheme and detailed. Furthermore, improved motion estimation algorithm is presented and AES processor. In section 3, statistical results of the secure video codec are shown. Finally conclusions are drawn in section 4. II. PROPOSED SECURE VIDEO CODEC This section presents the overflow of our secure video codec based on DWT and AES. Our goal is to provide up to 9 cipher schemes as know full encryption, 50% of transformed data is encrypted, LL band encrypted (25% of transformed image was encrypted), LL2 band encrypted (6,25% of transformed image was encrypted). The AES module is placed just after the wavelet transform in order to exploit the properties of wavelet to achieve a selective encryption. A. The Video Codec The proposed codec is a synthesis of JPEG and JPEG2000, it took Huffman coding to the JPEG and Discrete Wavelet Transform (DWT) from the JPEG2000. Figure 1 shows the block diagram of the different units of the proposed video codec. It includes the following units:

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Computer Science Section

Original Video

Control Unit

Spatial DWT

Motion Estimation (Proposed ARPS)

Temporal DWT

Quantized

Huffman Coding

the approximation will be computed at the corresponding positions in the previous frame, x2t.

Encoded Video

+

AES-128

K

AES-192

Split

AES-256

Prediction

Updating

Motion Information Vector

+ Figure 1. Block diagram of our Proposed Secure Video Encoder

ƒ Control Unit: that is designed to control the data flow in the design. The control unit coordinates all beginning system operations and process (Motion Estimation and Spatial Transform). ƒ Discrete Wavelet Transform (DWT) by Lifting Scheme (LS): it achieves better performances than DCT in terms of visual quality of HVS shown in [7]. ƒ Quantization: in our codec, the linear quantization is used; it is the most popular in many codec. This block reduces the coefficient information for coding in small bit number. ƒ Huffman coding: is used for coding the DWT coefficient after the quantization block. ƒ Motion Estimation: this module execution requires more time than the other blocks in all existing codec; therefore the choice is to improve Adaptive Rood Pattern Search (ARPS) with other BMAs. 1) DWT by Lifting Scheme The lifting scheme can be used for DWT implementation. It is constituted by predictions and updating steps. Figure 2 shows the block diagram of the DWT using lifting scheme. For 9/7 filter, we have four steps, two primary lifting steps (predictions) and two other dual lifting steps (updating), where K is the normalisation constant. However, 5/3 filters are based on one prediction and one updating. The results of image wavelet transformation are low frequencies coefficients (LL band) and high frequencies coefficients (HH band). 2) Temporal Transform Using 5/3 Filter We consider a simple 5/3 bi-orthogonal wavelet transform applied along the temporal axis, where both prediction and update operator have only two taps. For video processing, the temporal filtering can be performed as described above; except that Motion Compensated (MC) frames have to be used as samples. Special attention is required if fractional-pel Motion Estimation (ME) is performed and the prediction and update operators involve not only ME/MC but also interpolation. As in our approach, filtering is performed on MC frames, by predicting x2t+1 from x2t and x2t+2; it is obvious that, for all t∈{0, T-1}, we have to deal with two Motion Vector Fields (MVF): a forward MVF form x2t to x2t+1 and backward one from x2t+2 to x2t+1. We will also try to keep the same conventions as in the Haar filtering case, i.e. the ht frame will be computed at the same positions as x2t+1; while

K −1

Figure 2. Block diagram of lifting scheme

We introduce the following notations for the Motion Vectors (MV): the forward MV, predicting the position n in frame x2t+1 from the frame x2t will be denoted by v 2+t +1 (n) ; while the backward MV, predicting the same position in frame x2t+1, but from frame x2t+2 will be v 2−t +1 ( n ) (see figure 3). The expressions to calculate the following temporal filtering in the motion direction are shown in (1) and (2). ht (n) = x2t +1 (n) − αx2t (n − v2+t+1 (n)) + βx2t +2 (n − v2−t +1 ) (1)

[

lt ( p) = x2t ( p) + γht −1 ( p + v2−t +1 (m)) + δht ( p + v2+t +1 ))

]

(2)

3) Spatial Transform Using 9/7 Filter Subband coding provides an easy way to obtain spatial scalability as well. The special scalability cost should be also comparable to those of other scalabilities discussed in video codec like MPEG-4 and H.264. The only additional problem is linked to motion vectors which, in our coder, are not spatially scalable. A filtered and sub sampled version of input data can be considered, but, in this case, the performances would become dependent from low-pass filter used in spatial analysis. In our codec, we used the classical 9/7 wavelet filters. B. Motion Estimation Algorithm 1) Adaptive Rood Pattern Search (ARPS) A novel and simple fast block matching algorithm, called Adaptive Rood Pattern Search (APRS) consists of two sequential search steps is presented in [9] and we improve this algorithm in [10]. The initial search is utilized to locate a good starting point. In this way, the chance for being trapped by local minima is highly reduced and unnecessary intermediate search points can be skipped. The speed and accuracy of the rood pattern based search algorithm is highly related to the size of pattern. Calculating the statistical average of MVs in the ROS is a common approach for prediction of motion vector in current block. Experimental results show that two types of prediction criteria, the mean and median, where the cost function is Mean Squared Error (MSE) is given by equation (1) fairy yield similar results in terms of PSNR given by equation (2).

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Journal of Applied Computer Science & Mathematics, no. 9 (4) /2010, Suceava

x 2t

x 2 t +1

α

x2t +2

n

v

ht −1

p

v

Video sequences

n

− 2t −1

lt

ht

Figure 3. Predict (left) and update (right) operators in the spatio-temporal domain

MSE = PSNR

1 N2

N −1 N −1

∑ ∑ (C i=0 j =0

= 10 Log

10

− R ij )

2

ij

⎡ 255 2 ⎤ ⎢ ⎥ ⎣ MSE ⎦

Claire

Diskus

Alex

Tennis

Foreman

288x352

288x352

288x352x3

ES

204.2828

240x352x3

144x176x3

204.2828

210.3165

208.0162

TSS

195.9630

23.4718

23.3628

23.8543

23.6369

22.8413

NTSS

17.9062

18.8229

17.0561

19.9024

22.6735

SESTSS

16.8754

16.5787

16.9354

16.5906

15.6780

BMAs Ours Works

δ

γ m

TABLE 1. AVERAGE NUMBER OF SEARCH POINTS PER MOTION ESTIMATION VECTOR

ht v 2t+ +1

− 2t +1

β

v 2t+ +1

x 2t

Resolution

4SS

16.5741

17.3435

17.1018

17.9929

19.1705

(3)

DS

13.3532

14.4870

14.1372

15.6592

17.3941

ARPS

6.4298

7.7251

7.4432

8.8701

10.1337

(4)

IARPS

4.4567

5.6703

5.2440

5.6775

6.6554

Where N is the size of the macro block, Cij and Rij are the pixels being compared in current macro block and reference macro block respectively. The ARPS leads to the new search centre directly to the most promising area which is around the global minimum. Hence, instead of performing full search, a compact and small search pattern can be utilized to locate the global minimum. 2) Improved Adaptive Rood Pattern Search (IARPS) Our goal is to improve the speed of the block-matching by using ARPS algorithm developed in our previous work [11]. The method is to set the thresholds T and compare it at the MAD given by equation (5): ƒ The threshold T is fixed by the first MAD of the center block of the window search compared to the block of reference. ƒ When MAD ≥T: In this way, the search process for the next block and repeat this step. ƒ When MAD < T: The MAD point in this step is the final solution of the motion vector which points to the best matching block.

The legend of BMAs are implemented in our works is: Exhaustive Search (ES), Four Step Search (4SS), Three Step Search (TSS), Simple and Efficient Search extension to TSS (SESTSS), New Three Step Search (NTSS), Diamond Search (DS), Adaptive Rood Pattern Search (ARPS) and our Improved Adaptive Rood Pattern Search (IARPS). We can see from table I and figures 4 and 5: ƒ Compared with other fast BMAs like ARPS, the proposed IARPS achieves the smallest ASP with marginal degradation of PSNR. For example, for the Claire sequence, IARPS achieves 30,69% ASP speedup than ARPS while the PSNR decreases by 0,9 db. For the sequence tennis with relatively higher degree of motion, the IARPS algorithm gives more than 35,99% ASP speedup compared to ARPS while the PSNR decreases by 1,3db. ƒ High degradation for the 28th frame of Claire sequence (23th frame in tennis sequence) for all presented BMAs. This is due to the huge difference between these frames and the reference frames. Our improved codec joint these ideas to improve the speed of treatment of these codec and it can be compared with other popular codec like H264 developed in [12].

3) Performance results of the IARPS

D. AES Algorithm

In our simulation, the block size is fixed at 16x16. To make a consistent comparison, block matching is conducted within a 30x30 search window (i.e. ± 7 pixels displacement in horizontal and vertical direction). For the fast BMAs, computational complexity can be measured by the average number of search points per motion vector estimation. Table 1 illustrate the average search point per frame for some BMAs and our Improved Adaptive Rood Pattern Search (IARPS) algorithm (implemented in PC configuration: CPU 1.86 GHz, 512 Memory, and Microsoft Windows XP Professional).

AES algorithm is a block cipher with variable key length and block size of 128 bit. 42 ES DS ARPS IARPS

41

PSNR(dB)

40

39

38

37

36

35 0

5

10

15

20 25 Frame Number

30

35

40

Figure 4. Frame-based PSNR (db) performance of ES, DS, ARPS and IARPS with Claire sequence

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35 ES DS ARPS IARPS

34

Image Color R

PSNR(dB)

33

32

31

Frame n

Frame 1

Image Color G Ch = 0,052 Cv = 0,050

30

29

28 0

Image Color B 5

10

15

20 25 Frame Number

30

35

RGB Frame Original

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III. SECURITY ANALYSIS OF OUR CODEC BY STATISTICAL APPROACH Shannon suggested two methods of diffusion and confusion in order to frustrate the powerful attacks based on statistical analysis [13]. Statistical analysis has been performed on the proposed video encryption scheme, demonstrating its superior confusion and diffusion properties which strongly defend against statistical attacks. This is shown by a test on the histograms and the correlation of adjacent pixels in the ciphered frame.

RGB Frame Decrypted and Decoded

Figure 6. Video Encryption Process: Band LL Encrypted. Ch (Horizontal Correlation), Cv(Vertical Correlation) and PSNR

Figure 5. Frame-based PSNR (db) performance of ES, DS, ARPS and IARPS with tennis sequence

There are three kinds of choices for the cipher key of the AES: 128-, 192- and 256-bit, called AES-128, AES-192, and AES256, respectively. Our design has covered the three versions. The only differences are the number of rounds (Nr) performed and round keys needed. Nr is a number associated with the key length. In our case of variable key size, Nr equals 10, 12 or 14. AES consists of four transforms (SubBytes, ShiftRows, MixColumns, AddRoundKey) operating on bytes, rows and columns of the 4 x 4 byte array, called the state that represents the data block.

RGB Image Encrypted

Bands LL Encrypted

PSNR = 311,53 dB

2) Correlation and Entropy The table 2 present the results of the correlations in two directions and entropy of some popular frame video sequences: Foreman, Akiyo, Susie and Carphone respectively. The correlation in the both directional of pixels at different frame is maximal in the frame without encryption about equal unit in all video sequences. The correlations (horizontal and vertical) of ciphered frame are very less in order to 0.01; than decrease respectively to full encryption, LL band encryption at LL2 band encrypted. Therefore we can conclude the ciphered frames are very smoothness, this proves the highest security of our secure video codec. We remark the entropy increase to without encryption at the full encryption via LL band encrypted and LL2 band encrypted of them video sequences. Therefore the information decreases

A. Video Analysis In this section we analyse a selective encryption of some popular video sequences (Foreman, Susie, Carphone and Akiyo). Figure 6 shows the process of our encryption video scheme. Each component of colour image is treated like an image single and they three components are combined to obtain the encrypted image colour.

Original Frame

Histogram Original Frame

Encrypted Frame

Histogram Encrypted Frame

Figure 7. Histogram of original and ciphered (LL2 band is encrypted) Akiyo frame TABLE 2. CORRELATION AND ENTROPY COMPARAISON OF DIFFERENT VIDEO SEQUENCES

1) Histograms In figure 7 are shown the histograms of the famous Akiyo video sequences. We can see that the histogram of ciphered frame are significantly different compared to histogram of original frame for the Akiyo video sequence. Therefore the encrypted LL2 band gives highest secure of a video sequence.

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Horizontal Correlation

Vertical Correlation

Entropy

Without encryption

0,9246

0,9559

0.0018

Full encryption

0,0124

0,0143

3,5675

LL Band encrypted

0,0496

0,0507

2,8707

LL2 Band encrypted

0,0538

0,0508

2,2482

Video Sequences

Foreman

Journal of Applied Computer Science & Mathematics, no. 9 (4) /2010, Suceava

Susie

Carphone

0,9460

0,9612

0,0066

Full encryption

0,0157

0,0143

3,9854

LL Band encrypted

0,0843

0,0488

3,7829

LL2 Band encrypted

0,0877

0,0972

3,0827

Without encryption

0,9313

0,9653

0,0030

Full encryption

0,0213

0,0210

4,5432

LL Band encrypted

0,0484

0,0536

4,0445

ƒ First group (full encryption, 50% transformed data is encrypted, LL1) requires the largest security and the medium time encryption. TABLE 3.COMPRESSION RATIO WITH AND WITHOUT ENCRYPTION FRAME OF FOUR VIDEO SEQUENCES Frame of Sequence

Without encryption

Full encryption

12.6786

9,678

Selective encryption LL Band encrypted

LL2 Band encrypted

9.8308

11.2770

LL2 Band encrypted

0,0534

0,0536

2,7324

Foreman

Without encryption Full encryption

0,9573 0,0176

0,9754 0,0233

0,0073 4,6543

Akiyo

13.5678

9,8343

11.0901

12.5538

LL Band encrypted

0,0567

0,0546

4,3456

Susie

14.6755

10.8765

11.0307

13.1765

Carphone

14.5538

11.9879

12.7904

13.9765

LL2 Band encrypted

0,0564

0,0845

2,5732

respectively to without encryption, LL band encryption, LL2 Band encrypted and the full encryption. In the table III we show the compression ratio of with and without encryption of them four video sequences. In all frame sequences the highest compression ratio are obtained in without encryption frame. Therefore the encryption decrease the compression ratio of our video codec, but there are not significant 3% for foreman, 3,7% for Akiyo, 3,8% for Susie and 2,7% for Carphone in full encryption. The LL2 Band encrypted gives the less degradation of compression ratio in our simulation, this decrease to 1,4% for Foreman, 1% for Akiyo, 1,5% for Susie and 0,57% for Carphone. We can see the low degradation are observed in carphone only 0,57% in LL2 band encrypted.

Horizontal and Vertical Correlation Foreman 0.14 Vertical Corelated Horizontal corelated

Vertical Corelated Horizontal corelated

0.22

0.18

Correlation

LL2 (6,25%)

0.12

0.2

LL2 (6,25%)

0.16

0.1

0.14 0.12

LL(25%)

0.08

0.06

0.1

LL(25%)

0.08

0.04

0.06 0.04

0

10

20

30 40 50 60 70 Percentage of Encrypted Image(%)

80

90

100

0.02

0

10

20

30 40 50 60 70 Percentage of Encrypted Image(%)

80

90

100

Figure 8. Correlation sensitive tests: foreman and Akiyo video sequence ciphered

ƒ Second group (LL2 is encrypted, 12,5% transformed data is encrypted, LL3 is encrypted) requires the medium from low security and the low time encryption.

B. Performance of the Proposed Secure Video Codec Various experiments have been done to exam the performance of the proposed secure video codec. The test video sequences frames were decomposed into 6-level DWT. The proposed codec use the propriety of multi-resolution analysis of wavelet to achieve various purposes of selective encryption schemes. The decomposition multi-resolution of low frequency coefficients allow us to encrypt the essential information that expressed as a frame percentage. Figure 8 shows the horizontal and vertical correlation, for foreman and Akiyo video sequence, as a function of the frame percentage which is encrypted. From figure 8, the following comments can be drawn: ƒ Design of all AES ciphers schemes (AES-full encryption, AES-band LLi encryption) took from 0,04 (for AES-128 full encryption) to 0,06 (for AES-128 LL encryption) of the total correlation (0,93). It means that when the frame percentage encrypted is changed from 100% to 25% the correlation becomes slightly different. ƒ LL bands encryption approach cannot be blindly applied to any LLi bands. We can see, the correlation exceed 0,1 when the frame percentage encrypted is less 6,25% (LL2). Finally, we have found two classes of LL bands encryption schemes for our codec.

Akiyo

0.24

Correlation

Akiyo

Without encryption

IV. CONCLUSION In this work we have developed a video coder with multi level of security based on DWT and AES processor. Experimental results illustrate that the modified algorithm improves the search speed significantly which similar distortion of some motion algorithms. Furthermore, we extended the AES algorithm and applied it toward the cryptography of continuous video streams. To express the tradeoffs between security and real-time application requirements we have proposed and developed an AES processor with respect to two parameters; security and requirements. We have proposed a partial encryption technique based on AES-128 or AES-192 or AES-256. Taking advantage of the DWT in our codec, one may choose to encrypt LL band with security level goes from low to high. Increased protection is traded off against more encryption time. The percentage of data subject encryption while maintaining medium confidentiality is significantly reduced as compared to full encryption, the encryption of 6,25% (LL2 band) data already delivers a satisfying secure result. Experimental results demonstrate that the proposed secure codec is fast and is well suited to provide high security

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communication and high compression ratio with low latencies. REFERENCES [1] P. Melih and D. Vadi, “A MPEG-2-transparent scrambling technology”, IEEE Transactions on Consumer Electronics, vol. 48, 2002, pp.345–355. [2] L. Qiao and K. Nahrstedt, “Comparison of MPEG encryption algorithms”, International Journal on Computer and Graphics, Special Issue on Data Security in Image Communication and Network, vol. 22, 1998, pp.437–448. [3] C. Shi and B. Bhargava, “Light-weight MPEG video encryption algorithm”, in Proceeding of the international conference on Multimedia98, New Delhi, India, (1998), pp.55-61. [4] C. Shi and B. Bhargava, “A fast MPEG video encryption algorithm”, in Proceedings of the sixth ACM international conference on Multimedia98, 1998, pp.81–88. [5] L. Tang, “Methods for encrypting and decrypting MPEG video data efficiently”, in Proceedings of the fourth ACM international conference on Multimedia, 1996, pp.219–229. [6] W. Zeng and S. Lei, “Efficient frequency domain selective scrambling of digital video”, IEEE Transactions on Multimedia, vol. 5, 2003, pp.118– 129.

[7] D. Santa-Cruz and T. Ebrahimi, “A Stady of JPEG2000 Still Image Coding Versus Other Standards”, X European Signal Processing Conference, Tampere, Finland, (2000). [8] M. Zeghid, M. Machhout, L. Khriji, A. Baganne and R. Tourki, “A Modified AES Based Algorithm for Image Encryption”, International Journal of Computer Science and Engineering, vol. 1, 2007,pp.70-75. [9] B.-G. Kim, S.-T. Kim, S.-K Song and P.-S Mah,“ Novel Block Motion Estimation Based on Adaptive Search Patterns”, IEICE Trans. Information and Systems, vol. 89, 2006, pp. 1586–1591. [10] D. Dia, M. Atri, and R. Tourki “Improved Fast Motion Block Matching Based Adaptive Rood Pattern Search” 6th WSEAS International Conference on System Science and Simulation in Engineering, Venice, Italy, November 21-23, 2007. [11] D. Dia, M. Atri, and R. Tourki “A Improved Fast Motion Block Matching for Wavelet Video Coding” IEEE International Symposium, 2007, on Signal Processing and Information Technology. [12] J. Y. Fan, Y.-L. Zhang, Y. Wang and F.-G. Wang, “A Improved Search Algorithm of H264 Motion Estimation”, Journal of Physics Conference Series, vol. 48, (2006), 398–402. [13] C.-E. Shannon, “Communication theory of secrecy system”, Bell systems Tech. journal, vol. 28, 1949, pp. 656–715.

D. DIA received his M. S degree in electrical engineering from ENIT University of Tunis, Tunisia. He is currently a member of the Laboratory of Electronics & Micro-electronics. His research includes Image Processing, Compression, Video Coding. M. ZEGHID received his M.S. degree in Electronic Materials and Devices from the Science Faculty of Monastir, Tunisia, in 2005. Currently, he is a PhD student. His research interests include Security Networks, implementation of standard cryptography algorithm, Multimedia Application, Network on Chip: NoC. He is working in collaboration with LESTER Laboratory, Lorient, France. M. ATRI born in 1971, received his Ph.D Degree in Micro-electronics from the Science Faculty of Monastir in 2001. He is currently a member of the Laboratory of Electronics & Micro-electronics. His research includes Circuit and System Design, Image Processing, IPs and SoCs. B. BOUALLEGUE received his MSc in Physic Microelectronic and his DEA in Electronic Materials and Dispositifs from the Science Faculty of Monastir, Tunisia, in 1998 and 2000, respectively. Currently, he is a PhD student. His research interests include High Speed Networks, Multimedia Application, Network on Chip: NoC, flow and congestion control, interoperability and performance evaluation. He is working in collaboration with LESTER Laboratory, Lorient Cedex France. M. MACHHOUT was born in Jerba, on January 31 1966. He received MS and PhD degrees in electrical engineering from University of Tunis II, Tunisia, in 1994 and 2000 respectively. Dr Machhout is currently Assistant Professor at University of Monastir, Tunisia. His research interests include implementation of standard cryptography algorithm, key stream generator and electronic signature on FPGA. Rached TOURKI was born in 1948. He received the B.S. degree in Physics (Electronics option) from Tunis University, in 1970; the M.S. and the Doctorat de 3eme cycle in Electronics from Institut d'Electronique d'Orsay, Paris-south University in 1971 and 1973 respectively. From 1973 to 1974 he served as microelectronics engineer in Thomson-CSF. He received the Doctorat d'etat in Physics from Nice University in 1979. Since this date he has been professor in Microelectronics and Microprocessors with the physics department, Faculte des Sciences de Monastir. His research includes IP design, Image and Video Processing, cryptography and SoCs.

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