New Signal Security System for Multimedia Data Transmission Using Genetic Algorithms
Anil Kumar1 , Navin Rajpal2 and Akash Tayal3
1Anil Kumar, IT Department ,Bharati VidyaPeeth College of Engineering, Delhi-63
2 Dr. Navin Rajpal, IT Department ,Guru Gobind Singh Indraprastha University, Delhi.
3 Akash Tayal , ECE Department ,Guru Gobind Singh Indraprastha University, Delhi.
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Abstract
We propose a new signal security system for real time Multimedia data transmission application. This approach uses the concept of Genetic Algorithms along with NLFFSR (Non Linear Forward Feedback Shift Register) to encrypt the data stream and then transmit the encrypted data stream. The encryption by this method is such that the signal can be transformed into completely disordered data. The feature of the proposed technique includes high data security and high feasibility for easy integration with commercial Multimedia transmission application. The implementation results show that the proposed signal security system can achieve high throughput rate that is fast enough for real time data protection in Multimedia transmission application.
Index Terms-- Cryptography, NLFFSR, Secure Data Transmission, Encryption/Decryption.
1. Introduction
Recently, with the greater demand in digital signal transmission [1, 2] and the big losses from illegal data access, data security has become a critical and imperative issue in Multimedia data transmission application. In order to protect valuable data from undesirable readers or against illegal reproduction and modifications, there have been various data encryption techniques [3, 4, 5, 6, 7, 8, 9, 10] and the watermark embedding schemes [11, 12, 13] on images is proposed in the literature. The data encryption techniques make the images invisible to undesirable readers, while the watermark embedded scheme hide watermarks on to the image to declare their ownership but the image is still visible.
Among the existing data encryption techniques [3, 4, 5, 6, 7, 8, 9, 10], we can classify their basic design idea into three major types: position permutation [5, 6], value transformation [7, 8], and the combination form [9, 10]. The position permutation algorithms scramble the original data according to some predefined schemes. It is simple but usually has low data security. The value transformation algorithms transform the data value of the original signal with some kind of transformation. It has the potential oflow computational complexity. Finally, the combination form performs both position permutation and value transformation. It has the potential of high data security.
In this paper, we propose a new signals security system for real time Multimedia data transmission applications. The rest of the paper is organized as follows. In Section II, we define NLFFSR mechanism for generating pseudorandom binary sequences. In Section III, we define Genetic Algorithms. In Section IV, we explain the procedure of data encryption using Genetic Algorithm and NLFFSR. In Section V, we give the Illustrations and in the Section VI, we give Analysis of security problem and in the Section VII we discuss Results and basic comparison with other encryption methods. In section VIII we give conclusions. In the last Section IX, we have cited the references.
2. Non Linear Forward Feedback Shift Register?
A Non-Linear feedback shift Register (NLFFSR) is a mechanism for generating binary sequences [14, 15, 16]. Figure 1 shows a general model of an n-bit NLFFSR. It is a Non linear forward feedback shift register with a feedback function f.
Output Sequence
Nonlinear Function gFeedback Function f
Figure 1 A General model of 4 bit NLFFSR
NLFFSR are extremely good pseudorandom binary sequence generators [14, 15, 16]. When this register is loaded with any given initial value (except 0 which will generate a pseudorandom binary sequence of all 0s) it generates pseudorandom binary sequence which has very good randomness and statistical properties.
The only signal necessary for the generation of the binary sequence is a clock pulse. With each clock pulse a bit of the binary sequence is produced. A model of 4-bit NLFFSR is considered to demonstrate the functioning of NLFFSR with the feedback function f =1+x+x4 and the non linear function g defined by an-1.an-3 (+) an-2.an-4 forming non linear feed forward shift register generator. Its initial bit values are used (1111).
The output sequence Zn: 011111000000001……….. Generated by NLFFSR in is periodic of period 15, which is the same as the period of the sequence generated by NLFFSR of 4 bits.
Period of the sequence generated by NLFFSR is maximum if we use the primitive polynomial. To design any stream cipher system, one needs to consider the NLFFSR with primitive feedback polynomials as the basic building blocks. Period of the enciphering sequence can be increased if it is generated by following methods:
(1)Addition of maximal length sequences.
(2)Multiplication of maximal length sequences.
(3)Using multi logic generalized linear feedback shift register.
The usefulness of these sequences depends in large part on there having nearly randomness properties. Therefore such sequences are termed as pseudorandom binary sequences. The balance, run and correlation properties of these sequences make them more useful in the selection of secret keys [17, 18, 19]. The NLFFSR generated sequences are of great importance in many fields of engineering and sciences.
3. Genetic Algorithms
There is a large class of interesting problems for which no reasonably fast algorithms have been developed. Many of these problems are optimization problemsthat arise frequently inapplications. Given such a hard optimization problem it is often possible to find an efficient algorithm whose solution is approximately optimal. For some hard optimization problems one can use probabilistic algorithms as well. These algorithms do not guarantee the optimum value, but by randomly choosing sufficiently, many “witnesses” the probability of error may be made as small as we like. For small a spaces, classical exhaustive methods usually suffice; for larger space special artificial intelligence techniques must be employed. Genetic Algorithms (GA) are among such techniques; they are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strife for survival. All genetic algorithms contain three basic operators: reproduction, crossover, and mutation, where all three are analogous to their namesakes in genetics [20]. Reproduction and crossover together give genetic algorithms most of their searching power. Genetic algorithms try to perform an intelligent search for a solution from a nearly infinite number of possible solutions. GA have been quite successfully applied to optimization problems like wire routing, scheduling, adaptive control, game playing, cognitive modeling, transportation problems, traveling salesman problems, optimal control problems, database query optimization, etc.
1 / 0 / 0 / 0 / 1 / 0 / 1 / 00 / 1 / 1 / 0 / 1 / 0 / 0 / 1
(a)
1 / 0 / 0 / 0 / 1 / / 0 / 1 / 00 / 1 / 1 / 0 / 1 / 0 / 0 / 1
(b)
1 / 0 / 0 / 0 / 1 / 0 / 0 / 10 / 1 / 1 / 0 / 1 / 0 / 1 / 0
(c)
Figure 3. Illustration of the Crossover Operation
Crossover is the process in which the strings are able to mix and match their desirable qualities in a random fashion. Crossover proceeds in three simple steps
- Two new random strings are selected (Fig. 3a).
2. A random location in both strings is selected (Fig. 3b).
- The portions of the strings to the right of the randomly selected location in the two strings are exchanged (Fig. 3c).
In this way information is exchanged between strings, and portions of two strings are exchanged and combined.
4. Data Encryption Using Genetic Algorithms
The encrypting process will emulate the working of the crossover operator in the genetics. To select the two strings to which the crossover operator is to be applied we take two consecutive bytes of the data stream, and to select the random position at which the crossover will take place we use NLFFSR generated pseudo random binary sequence. The binary sequence is taken as the pair of three which gives the decimal value ranging from 0 to 7 which means we can do the crossover at any position in the two bytes. The nature of the pseudo random binary sequence ensures that we get equally random decimal sequence corresponding to which we will hide the data stream in the image. This makes our encrypting process very safe and secure. We use the randomness of the NLFFSR generated sequences with the Genetic Algorithms to encrypt the data stream.
Let two consecutive bytes of the data stream A & B are having 11100010 and 11001001 values respectively. Let random decimal value sequence as generated by the NLFFSR is 2,3,1,5…
Before operation is performed, the values are
A = 11100010
B = 11001001
Operator is applied using random value 2.
After operation is performed, the values are
A = 11100001
B = 11001010
5. Illustrations
6. Analysis Of Security Problem
It is of interest to know if the proposed technique is easily decrypted or not. This security problem is analyzed in the following.
Since there are M combinations to encrypt 2 consecutive data bytes, thus the number of possible encryption result is M (N/2), where N is the total number of the data bytes to encrypt and M is the length of one data byte.
For example, consider an image of size 256*256 pixels and color depth of 8 bit per pixel. In this case M equals 8 and N equals 65536. All the possibilities are 832768. Since the NLFFSR pseudorandom binary sequence is unpredictable [17, 18, 19], it is very difficult to decrypt correctly an encrypted signal by making an exhaustive search without knowing the initial value and the feedback function f and Non Linear output function g of the NLFFSR.
7. Results
In the simulation, ten images are used. As representatives, only the images of “lena”, “it_logo” are shown in figures 4(a), 4(d)), respectively. The most direct method to decide the disorderly degree of the encrypted image is by the sense of sight. On the other hand, the fractal dimension [21, 22] can provide the quantitative measure on the randomness of the encrypted image. To measure how rough the encrypted image surface is, its fractal dimension is calculated according to the method in [22].
The encrypted result of the four representative images by this method are shown in figures 4(b), 4(e).
According to the figure 4, the encryption results of the method are completely disordered and cannot be distinguished from the original image. The figures 4(c), 4(f), respectively, so the decrypted image of “lena”, “it_logo”. Since the proposed method is losable, we can find that there would be no encryption/decryption errors in using the proposed technique.
This method create more randomness compare to the other existing method. So the recovery of the Data is more complex compare to any other method.
8. Conclusion
In this paper, we have proposed a new signal security system for Multimedia communication. The use of Genetic Algorithms in cryptography gives us very strong encrypting algorithm used with the randomness properties of NLFFSR. This total way of transferring secret information is highly safe and reliable, by using and mixing the best methods we achieve the total security in the field of Multimedia communication. We are currently planning to design a very sophisticated software based on this technique which will targeted to use in highly secure Multimedia data transmission systems. The simulation result have indicated that (1) the encryption results are completely chaotic by the sense of sight, and (2) the encryption results are very sensitive to the parameter fluctuation. The success of the purposed security system is because of its high throughput rate which makes it very suitable for real time Multimedia data transmission.
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