# The Secure Transmission of Videos Using the Karhunen-Loéve (K-L) Decomposition and the Synchronization of the Unified Chaotic System with the Hyperchaotic Chen System

Authors: N. Smaoui; M. Zribi; T. Elmokadem
Journal: Mathematical Problems in Engineering
 (2018)

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## Abstract

A unique secure communication scheme that can be used for the transmission of gray-scale and color videos is presented in this paper. The proposed scheme is developed by using the Karhunen-Loéve (K-L) decomposition and the synchronization of the unified chaotic system with the hyperchaotic Chen system. First, the gray-scale or color video is represented by a set ofN frames. In order to reduce the data, the K-L decomposition is used to come up with data coefficients and eigenfunctions that optimally obtain the crux of the N frames. Using only the most energetic eigenfunctions to approximate the original frames results in computational savings. The data coefficients corresponding to the most energetic eigenfunctions are encrypted and transmitted using a master system composed of a combination of the unified chaotic system and the hyperchaotic Chen system. At the receiver end, these coefficients are recovered and a controller of the sliding mode type is utilized forcing the master and slave systems to synchronize. Simulation results illustrate how the proposed control law is able to synchronize the master and the slave systems. In addition, a demonstration of the recovery of the original frames using the decrypted data coefficients along with the eigenfunctions of the frame is provided. The presented simulations indicate that the proposed scheme results in an excellent performance.


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## Body

## 1. Introduction

Historically, the field of chaos synchronization has attracted many researchers from an array of disciplines [1–31] due to a number of applications which utilize chaos. Secure communication systems are among the potential applications of chaos synchronization. The concept relies on the idea of hiding the transmitted signal in the states of the transmitter’s chaotic system. Retrieving the transmitted signal requires control or synchronization of the chaotic system at the transmitter end with the chaotic system at the receiver end.Chaotic systems are extremely sensitive to initial conditions; this fact makes their dynamical behaviors unpredictable. Ott et al. [1] were the first to introduce the idea of control of chaos by forcing the behavior of chaotic dynamical system to follow a desired behavior. Pecora and Carroll [2] demonstrated that chaotic systems can be synchronized using a drive-response approach. Since then, chaotic synchronization methods have become very attractive in many fields.In 1993, the chaotic modulation method was applied by Wu and Chua [3] in secure communication, and in 1996, chaotic parameter modulation method was introduced by Yang and Chua [4]. In 2005, Lu and Cao [7] designed adaptive controllers to synchronize the states of the chaotic Lorenz system with the states of the hyperchaotic Chen system. Moreover, adaptive control laws were then designed by Park [8] to synchronize two hyperchaotic Chen systems using Lyapunov stability theory. Recently, different control designs have been investigated to synchronize hyperchaotic and chaotic systems have been examined; for example, see [9–33].In the past few years, many researchers have proposed various images/videos encryption techniques to encrypt and securely transmit images/videos [34–48]. In 2015, Lin Z. et al. [41] proposed a systematic methodology for real-time video encryption and decryption system using a chaotic map-based; the system was implemented using an advanced RISC Machine (ARM) with embedded hardware. Ganesan K. et al. [42] proposed multicore CPUs and GPUs to facilitate a secure video transmission. In 2017, Chen S. et al. [40] proposed a systematic methodology based on chaos for video encryption and decryption in real-time.This paper proposes an unprecedented secure communication scheme developed by using the K-L decomposition and the synchronization of hyperchaotic Chen with the unified chaotic systems for transmitting gray-scale and color videos. A promising advantage of using the unified chaotic system is its ability to have different behaviors and dynamical properties for different values of system parameter [10]. First, we represent each of the gray-scale and color videos by a set of N frames. Then, the K-L decomposition is utilized to generate data coefficients and eigenfunctions that optimally capture the original N frames. The data coefficients corresponding to the most energetic eigenfunctions are encrypted and transmitted using a master system composed of a combination of the unified chaotic system and the hyperchaotic Chen system. Even though the idea of using synchronization of hyperchaotic systems for secure communication is available in the literature, the novelty of this work is in the proposed scheme of decomposing the transmitted data before rather than masking the data directly as will be shown later. A sliding mode controller is then used at the receiver end to synchronize the two systems consisting of hyperchaotic Chen and unified chaotic systems to recover these data coefficients and thus reconstruct the transmitted videos. The sliding mode technique is chosen due to its popularity, robustness, finite-time convergence, and simplicity in implementation.The rest of the paper is structured as follows: The Karhunen-Loéve decomposition is described in Section2. Section 3 presents the unified chaotic system and the hyperchaotic Chen system. The design of the proposed controller is detailed in Section 4. Section 5 illustrates the proposed secure communication scheme. Simulation results validating the developed scheme are presented and discussed in Section 6. Finally, some concluding remarks are provided in Section 7.


## 2. The Karhunen-Loéve Decomposition

The K-L decomposition known as principal component analysis [49], factor analysis [50], proper orthogonal decomposition [51], singular value decomposition [52], quasiharmonic modes [53], and Hoteling transform [54] has wide applications in problems related to feature identification and data compression [51, 52, 55–63]. Since K-L decomposition was extensively discussed in many research work, we will only describe its main idea.First, the data is considered to be a sequence ofN real-valued vectors {Xi}i=1N, where the dimension of Xi is M such that Xi=[X1i,X2i,⋯,XMi]T. These vectors can represent a set of images.Using the snapshot method [64], one can compute the symmetric and positive definite covariance matrix B as follows:(1)B=Bij=1MXi,Xj,i,j=1,…,N.In (1), 〈·,·〉 denotes the usual Euclidian inner product.The eigenfunctions of the data are orthogonal and defined such that(2)Ψk=∑i=1NVikXi,k=1,…,N,where Vi[k] is the ith component of the kth eigenvector. Let X be such that(3)X=∑i=1NCiΨi.In (3), Ψi is the ith eigenfunction and Ci(i=1,…,N) are the data coefficients computed by projecting the data vector onto an eigenfunction such that(4)Ci=X·ΨiΨi·Ψi,i=1,…,N.The energy of the data,E, is defined as follows:(5)E=∑i=1Nλi,where λi is the eigenvalue of the ith eigenfunction. Because the energy percentage of each eigenfunction depends on the eigenfunction’s associated eigenvalue, λk, it can be calculated as follows:(6)Ek=λkE.In case that all the eigenfunctions are used, then the original data can be fully restored. Moreover, using the most energetic eigenfunctions, an approximation of the original data can be reconstructed as follows:(7)X~=∑i=1KCiΨi,whereK<N.


## 3. System Description

This section presents the hyperchaotic Chen system and the unified chaotic system needed for the development of the secure communication scheme; the hyperchaotic Chen system as well as the unified chaotic system is used to generate the master and slave systems.The master system is obtained by combining the hyperchaotic Chen system with the unified chaotic system. The hyperchaotic Chen system is a fourth-order system of ODEs defined such as(8)x˙=ay-x+wy˙=dx-xz+cyz˙=xy-bzw˙=yz+rwwhere x, y, z, and w represent the system states and the scalars a, b, c, d, and r are the parameters of the system. This system exhibits a hyperchaotic behavior when a=35, b=3, c=12, d=7, and 0≤r≤0.085 [10].The unified chaotic system is defined such that(9)x˙u=25α+10yu-xuy˙u=28-35αxu-xuzu+29α-1yuz˙u=xuyu-α+83zu,where xu, yu, and zu are the system states and α is a real parameter satisfying α∈[0,1]. Depending on the value of α, system (9) can display different behaviors and dynamical properties. For instance, system (9) exhibits the behavior of the Lorenz chaotic system when α=0 [55]. When 0<α<0.8, system (9) becomes the generalized Lorenz chaotic system. System (9) will be the Lu¨ chaotic system if α=0.8 [6]. The unified chaotic system becomes the Chen chaotic system when α=1 [5]; and it becomes the generalized chaotic system when 0.8<α<1.Therefore, the master system is defined as follows:(10)x˙m=aym-xm+wmy˙m=dxm-xmzm+cym-Dz˙m=xmym-bzmw˙m=ymzm+rwmx˙u1=25α+10yu1-xu1y˙u1=28-35αxm-xmzu1+29α-1yu1z˙u1=xmyu1-α+83zu1where xm, ym, zm, and wm represent the states of the Chen hyperchaotic system (A) with a,b,c,d, and r being its parameters. The states of the unified chaotic system (B) are xu1,yu1, and zu1, and α is the system parameter. Also, D=γm(t) represents the message m(t) to be transmitted (data coefficients) with a scaling factor γ. The overall system is depicted in Figure 1.Figure 1
A depiction of the proposed communication scheme.At the receiver side, the slave system is taken to be a combination of the hyperchaotic Chen system (C) and the unified chaotic system (D) (see Figure1). It should be noted that the parameters of the master and slave systems should be the same.Hence, the description of the slave system can be written as follows:(11)x˙s=ays-xs+ws+u1y˙s=dxs-xszs+cys+u2z˙s=xsys-bzs+u3w˙s=yszs+rws+u4x˙u2=25α+10yu2-xu2y˙u2=28-35αxm-xmzu2+29α-1yu2z˙u2=xmyu2-α+83zu2where xs, ys, zs, and ws are the states of the Chen hyperchaotic system (C), xu2,yu2, and zu2 are the states of the unified chaotic system (D), and the parameters a,b,c,d, and r are the same as for system (10). Also, the controllers of the slave system are u1,u2,u3, and u4. These controllers will be designed to synchronize the master and slave systems at the transmitter and receiver sides, respectively.It can be seen from (10) and (11) that the signal xm(t) is used to drive the two unified chaotic systems at both sides. Also, the parameter α used in both systems is generated from xm(t) using a generation rule that must be the same at the transmitter and receiver sides.


## 4. Controller Design

A sliding mode control law is proposed in this section to synchronize the master and slave systems described in (10) and (11), respectively. This design is validated and proven to synchronize the two systems. The proof is divided into two parts. The first part of the proof shows that the signal xm(t) will synchronize the unified chaotic system (D) at the receiver end with the one at the transmitter side. The second part of the proof demonstrates that the proposed control law is able to synchronize the system at the receiver side with the one at the transmitter side.The unified chaotic systems at both the transmitter and the receiver sides are described, respectively, as follows:(12)x˙u1=25α+10yu1-xu1y˙u1=28-35αxm-xmzu1+29α-1yu1z˙u1=xmyu1-α+83zu1and(13)x˙u2=25α+10yu2-xu2y˙u2=28-35αxm-xmzu2+29α-1yu2z˙u2=xmyu2-α+83zu2.Notice that the signalxm(t) is driving the two systems. The errors between the two unified chaotic systems are defined such that(14)exu=xu2-xu1eyu=yu2-yu1ezu=zu2-zu1By differentiating these errors and using (12) and (13), we obtain the following:(15)e˙xu=25α+10eyu-exue˙yu=-xmezu+29α-1eyue˙zu=xmeyu-α+83ezu.Theorem 1.
If the parameterα is designed to be in the interval [0,1/29), the signal xm(t) will synchronize the unified chaotic system (13) at the receiver with the unified chaotic system (12) at the transmitter (i.e., limt→∞exu=limt→∞eyu=limt→∞ezu=0) [10].Proof.
Let the Lyapanov function candidateV1 be such that [10](16)V1=12125α+10exu2+121-29αeyu2+ezu2=12eTPewhere e=[exu,eyu,ezu]T and P is such that (17)P=125α+10000121-29α000121-29α
Clearly, the matrixP is positive definite when α is in the interval [0,1/29). This implies that V1>0 when α∈[0,1/29).
The derivative ofV1 with respect to time along the trajectories of the error system (15) is(18)V˙1=125α+10exue˙xu+121-29αeyue˙yu+ezue˙zu=exueyu-exu+121-29αeyu-xmezu+29α-1eyu+ezuxmeyu-α+83ezu=exueyu-exu2-12eyu2-α+861-29αezu2=-eTQewhere Q is defined as(19)Q=1-120-1212000α+861-29α.When α∈[0,1/29), the matrix Q is positive definite which implies that V˙1 is negative definite. Therefore, it is guaranteed that the errors exu, eyu, and ezu asymptotically converge to zero when α∈[0,1/29). In conclusion, the signal xm(t) will force the slave system (13) to be synchronized with the master system (12).Hence, after a timeTs, the unified chaotic systems will be synchronized, and the states xu1, yu1, and zu1 can be retrieved at the receiver side. These states are then used to unmask the states yme, zme, and wme at the receiver side to obtain the states of the Chen system at the transmitter side as follows:(20)ymdt=ymtzmdt=zmtwmdt=wmt,fort>Ts.Let the synchronization errors between the two Chen systems be such that(21)ex=xs-xmey=ys-ymez=zs-zmew=ws-wm.By differentiating these errors with respect to time and using (10) and (11), we obtain the following error system:(22)e˙x=aey-ex+ew+u1e˙y=dex-zsex-xsez+exez+cey+D+u2e˙z=xsey+ysex-eyex-bez+u3e˙w=zsey+ysez-eyez+rew+u4e˙xu=25α+10eyu-exue˙yu=-xmezu+29α-1eyue˙zu=xmeyu-α+83ezu.Theorem 2.
Let the controllers be designed such that(23)u1=-aey-ex-ew-K1sign⁡exu2=-dex+zsex+xsez-exez-cey-K2sign⁡eyu3=-xsey-ysex+eyex+bez-K3sign⁡ezu4=-zsey-ysez+eyez-rew-K4sign⁡ew.If the above controllers are applied to the slave system (11) at the receiver, the asymptotic convergence of the synchronization errors to zero is guaranteed.Proof.
Consider the following Lyapunov function candidate,(24)V2=12ex2+12ey2+12ez2+12ew2Using the proposed controllers in (23), the derivative of V2 with respect to time along the trajectories in (22) can be written as follows:(25)V˙2=exe˙x+eye˙y+eze˙z+ewe˙w=exaey-ex+ew+u1+eydex-zsex-xsez+exez+cey+D+u2+ezxsey+ysex-eyex-bez+u3+ewzsey+ysez-eyez+rew+u4=-K1exsign⁡ex-K2eysign⁡ey+Dey-K3ezsign⁡ez-K4ewsign⁡ew≤-K1ex-K2-D2ey-K3ez-K4ew
It is evident from (25) that V˙2<0 for K2>|D2|. Therefore, the asymptotic convergence of ex, ey, ez, and ew is guaranteed. Furthermore, if α∈[0,1/29), it is ensured that exu, eyu, and ezu will asymptotically converge to zero as well according to (1). Thus, the proposed controllers force the synchronization errors to converge to zero asymptotically.As a result, the proposed controllers in (23) guarantee that the slave system is synchronized with the master system; a noisy version of the transmitted message can be recuperated according to the equation m~(t)=ys-ym.Remark 3.
The signalxm(t) is used to generate the parameter α(t) to make sure that α(t)∈[0,1/29]. One of the possible generation rules that can be used is α(t)=|xm|/29(|xm|+1). Note that the α generation rule must be the same at both the transmitter and receiver sides.


## 5. The Proposed Secure Communication Scheme

A unique secure communication scheme developed by using the K-L decomposition and the synchronization of a combined hyperchaotic Chen system and the unified chaotic system is examined in this section. The developed scheme is utilized to transmit gray-scale and color videos through the encryption of the transmitted data. Figure1 depicts a block diagram of the proposed secure communication scheme. The description of the scheme is as follows.Consider a video (we will call it data) that requires secure transmission. We apply a separation rule to the data such that(i)
If the data is a gray-scale video, then the data is decomposed intoN frames. The gray-scale frames are represented by an m×n data matrix containing the gray levels of the pixels.(ii)
If the data is a color video, then it is decomposed intoN frames. The color frames (RGB frame) are represented using an m×n×3 data matrix which specifies the levels of red, green, and blue color components for each pixel.Using the Karhunen-Loéve decomposition on theN frames, one can generate the data coefficients and the eigenfunctions of the data. The eigenfunctions are transmitted without encryption through a public channel; this is the case because it is impossible to recreate the original data using only the eigenfunctions. To encrypt the data coefficients, we add them to one of the states in the master system. This is done by transforming the data coefficients into binary format to form a sequence of pulses γm(t) where the scalar γ represents the amplitude of the pulses. The sequence of pulses is appended to the state ym of the master system to be encrypted. The states of the new system are then transmitted to the receiver through a noise-free public channel.It is well known that hyperchaotic systems are extremely sensitive to initial conditions; hence it will not be possible to recover the original data using the transmitted states even if these states are intercepted. The reason for that is that the eigenfunctions of the original data are not known to the intruder who successfully intercepts the master system even in the case when the data coefficients are successfully retrieved. Hence, the proposed scheme for data transmission is very secure.In Figure1, the master system at the transmitter is considered to be a combination of the hyperchaotic Chen system (System A) and transmitted directly through the public channel. The states of the unified chaotic system (System B) at the transmitter (xu1, yu1, and zu1) are used to mask the ym, zm, and wm states of the hyperchaotic Chen system (System A) producing the masked states yme, zme, and wme, respectively. This stage is considered to increase the complexity of the proposed encryption to ensure that intruders are unable to retrieve any information from the transmitted data. The masked states are then transmitted to the receiver through the public channel. It should be noted that the masking rules should be the same at the transmitter and receiver sides.The receivedxm state is utilized to drive the unified chaotic system (System D) and is used to generate the parameter α(t) with the same generation rule that is used at the transmitter side. After the synchronization between the two unified chaotic systems, the states xu1, yu1, and zu1 are retrieved at the receiver side. These states are then utilized to unmask the received masked states yme, zme, and wme to obtain the states ym,zm, and wm. Using these generated states, a controller which is based on the sliding mode technique is used to synchronize the hyperchaotic Chen system (System C) at the receiver with the one at the transmitter. After the synchronization, a noisy version of the transmitted message mc(t) (data coefficients) can be retrieved and filtered in order to reconstruct the original message m~(t).Once we obtain the retrieved messagem~(t), we can recover the transmitted binary data coefficients and convert them back to real values. The obtained real-valued data coefficients along with the received eigenfunctions are used to recreate the original transmitted video by using (7).


## 6. Simulation Studies

Two simulation cases of the proposed communication scheme are presented in this section. The first case deals with the transmission of gray-scale videos while the second case deals with the transmission of color videos. In each of the two cases, the original video is decomposed intoN=49 frames; each frame is represented by a 120×160 data matrix for each of the gray-scale frames and for the color frames. These two cases are presented in the following two subsections.


### 6.1. Transmission of a Gray-Scale Video

In this subsection, we present the case of transmitting a gray-scale video using the proposed secure communication scheme. We consider a video from the Matlab toolbox of a traffic camera that observes vehicular behavior on a road for about 3.25 seconds. This video has 49 frames with a dimension of120×160 pixels, and these frames are shown in Figure 2.Figure 2
The frames of the original gray-scale video to be transmitted using the proposed scheme.First, the K-L decomposition is applied on these 49 frames to obtain 49 eigenfunctions. Figure3 depicts the most 16 energetic eigenfunctions. The generated 16 most energetic eigenfunctions are transmitted using a public channel. The energy associated with each eigenfunctions is shown in Figure 4. Figure 4(a) depicts the energy of all eigenfunctions while Figure 4(b) illustrates the energy associated with the remaining eigenfunctions not including the first one. It is noted from this figure that the first eigenfunction captures most of the energy (about 97.13 %).Figure 3
The most 16 energetic eigenfunctions.Figure 4
(a) Energy associated with all eigenfunctions. (b) Energy associated with all the eigenfunctions except the first one (gray-scale video).
(a) (b)The data coefficients are converted into binary numbers, and a sequence of pulses with a periodTp=1 is formed to represent the data coefficients. That is, the formed message is such that (26)mt=1ifthebitis1∀t∈t0,t0+Tp0ifthebitis0∀t∈t0,t0+TpThe messagem(t) is added to the master system at the transmitter in the ym state. Additionally, we define the generation rule of the α parameter to be such that(27)αt=xm29xm+1.This choice of the α generation rule ensures that α∈[0,1/29). Then the produced states of the unified chaotic system xu1, yu1, and zu1 are used to mask the states ym, zm, and wm. The masking rules are chosen in the following recursive manner:(28)yme=ym+xu1xu1+1zme=yme+zm-yu1yu1+1wme=zme+wm+zu1zu1+1.After adding the message to the master system, masking the states, and transmitting them, the proposed controller forces the slave system to synchronize with the master system. This fact can be clearly seen from Figures5 and 6; Figure 5 depicts the synchronization errors of the Chen systems while Figure 6 depicts the synchronization errors of the unified chaotic systems. It is evident that all errors converge to zero except ey which contains a noisy version of the transmitted data. Therefore, the original data is recovered from ey after filtering it. Part of the recovered message is shown in Figure 7.Figure 5
The errors of the Chen systems versus time for 50 seconds (gray-scale video).Figure 6
The errors of the unified chaotic systems versus time for 50 seconds (gray-scale video).Figure 7
The recovered signalmc(t) versus time for 100 seconds (gray-scale video).Using the recovered message, the data coefficients are then recovered and utilized alongside the received eigenfunctions in order to reconstruct the original vectors according to (7) which are reshaped to 120×160 arrays to reconstruct the original frames. The reconstructed frames using all eigenfunctions are presented in Figure 8. This clearly verifies the efficiency of the proposed scheme.Figure 8
The recovered gray-scale video using all eigenfunctions.To demonstrate that the developed communication scheme has another attribute besides being secure, the transmitted video can be reconstructed using some of the eigenfunctions (see Figure9). Figure 9 depicts the reconstructed video using the most 35 energetic eigenfunctions along with their corresponding data coefficients. Furthermore, the Peak Signal-to-Noise Ratio (PSNR) [65], which is a known video quality metric, was used to compare the quality of the reconstructed videos for different cases. The calculated PSNR values are 51.6 dB, 40.3 dB, 36.8 dB, and 27.5 dB when using 49, 35, 30, and 15 most energetic eigenfunctions, respectively. It should be noted that PSNR was calculated by comparing each frame with its reference and then taking the average for the whole video. It can be seen from these results that a better quality can be obtained by using more eigenfunctions, and the best case is by using all eigenfunctions.Figure 9
The recovered gray-scale video using the most 35 energetic eigenfunctions.


### 6.2. Transmission of a Color Video

In this subsection, we consider the transmission of a color video using the developed communication scheme. The color version of the traffic video used in Section6.1 is utilized here. Figure 10 presents the frames of the color video which are represented by a 120×160×3 data matrices.Figure 10
The frames of the original color video to be transmitted using the proposed scheme.First, K-L decomposition is applied on these 49 frames to obtain 49 eigenfunctions. Figure11 depicts the most 16 energetic eigenfunctions. The generated 16 most energetic eigenfunctions are transmitted using a public channel. Figure 12 depicts the energy associated with each eigenfunctions. Figure 12(a) shows the energy of all eigenfunctions while Figure 12(b) depicts the energy associated with the rest of the eigenfunctions not including the first one.Figure 11
The most 16 energetic eigenfunctions.Figure 12
Energy associated with the eigenfunctions (color video).
(a) (b)Next, we transform the obtained data coefficients into binary to form a sequence of pulses as described in Section6.1. This sequence of pulses is added to the ym state of the master system and all the states of the system are masked and transmitted. The sliding mode control law at the receiver end is used to synchronize the master and slave systems; see Figures 13 and 14. A noisy version of the transmitted data is then recovered by using ey where a part of that message is shown in Figure 15.Figure 13
The errors of the Chen systems versus time for 50 seconds (color video).Figure 14
The errors of the unified chaotic systems versus time for 50 seconds (color video).Figure 15
The recovered signalmc(t) versus time for 100 seconds (color video).Once the sent message is recovered, it is transformed back from binary to obtain the real-valued data coefficients. The recovered data coefficients are used in conjunction with the received eigenfunctions to reconstruct the data vectors using (7). Finally, the data vectors are reshaped to retrieve the transmitted color frames. The retrieved frames using all eigenfunctions which proves the efficiency of the developed communication scheme are presented in Figure 16.Figure 16
The recovered video using all eigenfunctions (color video).The reconstructed frames when only the most 35 energetic eigenfunctions are used are shown in Figure17. Moreover, the PSNR was calculated as a quality metric by considering different cases using 49, 35, 30, and 15 most energetic eigenfunctions for transmission and reconstruction. The corresponding PSNR values are 56.7 dB, 41.8 dB, 37.4 dB, and 27.5 dB, respectively. It is obvious that the reduction of transmitted eigenfunctions and coefficients comes at the expense of the reconstructed video quality which is a trade-off that can be made depending the desired requirements.Figure 17
The recovered video using the most 35 energetic eigenfunctions (color video).


## 6.1. Transmission of a Gray-Scale Video

In this subsection, we present the case of transmitting a gray-scale video using the proposed secure communication scheme. We consider a video from the Matlab toolbox of a traffic camera that observes vehicular behavior on a road for about 3.25 seconds. This video has 49 frames with a dimension of120×160 pixels, and these frames are shown in Figure 2.Figure 2
The frames of the original gray-scale video to be transmitted using the proposed scheme.First, the K-L decomposition is applied on these 49 frames to obtain 49 eigenfunctions. Figure3 depicts the most 16 energetic eigenfunctions. The generated 16 most energetic eigenfunctions are transmitted using a public channel. The energy associated with each eigenfunctions is shown in Figure 4. Figure 4(a) depicts the energy of all eigenfunctions while Figure 4(b) illustrates the energy associated with the remaining eigenfunctions not including the first one. It is noted from this figure that the first eigenfunction captures most of the energy (about 97.13 %).Figure 3
The most 16 energetic eigenfunctions.Figure 4
(a) Energy associated with all eigenfunctions. (b) Energy associated with all the eigenfunctions except the first one (gray-scale video).
(a) (b)The data coefficients are converted into binary numbers, and a sequence of pulses with a periodTp=1 is formed to represent the data coefficients. That is, the formed message is such that (26)mt=1ifthebitis1∀t∈t0,t0+Tp0ifthebitis0∀t∈t0,t0+TpThe messagem(t) is added to the master system at the transmitter in the ym state. Additionally, we define the generation rule of the α parameter to be such that(27)αt=xm29xm+1.This choice of the α generation rule ensures that α∈[0,1/29). Then the produced states of the unified chaotic system xu1, yu1, and zu1 are used to mask the states ym, zm, and wm. The masking rules are chosen in the following recursive manner:(28)yme=ym+xu1xu1+1zme=yme+zm-yu1yu1+1wme=zme+wm+zu1zu1+1.After adding the message to the master system, masking the states, and transmitting them, the proposed controller forces the slave system to synchronize with the master system. This fact can be clearly seen from Figures5 and 6; Figure 5 depicts the synchronization errors of the Chen systems while Figure 6 depicts the synchronization errors of the unified chaotic systems. It is evident that all errors converge to zero except ey which contains a noisy version of the transmitted data. Therefore, the original data is recovered from ey after filtering it. Part of the recovered message is shown in Figure 7.Figure 5
The errors of the Chen systems versus time for 50 seconds (gray-scale video).Figure 6
The errors of the unified chaotic systems versus time for 50 seconds (gray-scale video).Figure 7
The recovered signalmc(t) versus time for 100 seconds (gray-scale video).Using the recovered message, the data coefficients are then recovered and utilized alongside the received eigenfunctions in order to reconstruct the original vectors according to (7) which are reshaped to 120×160 arrays to reconstruct the original frames. The reconstructed frames using all eigenfunctions are presented in Figure 8. This clearly verifies the efficiency of the proposed scheme.Figure 8
The recovered gray-scale video using all eigenfunctions.To demonstrate that the developed communication scheme has another attribute besides being secure, the transmitted video can be reconstructed using some of the eigenfunctions (see Figure9). Figure 9 depicts the reconstructed video using the most 35 energetic eigenfunctions along with their corresponding data coefficients. Furthermore, the Peak Signal-to-Noise Ratio (PSNR) [65], which is a known video quality metric, was used to compare the quality of the reconstructed videos for different cases. The calculated PSNR values are 51.6 dB, 40.3 dB, 36.8 dB, and 27.5 dB when using 49, 35, 30, and 15 most energetic eigenfunctions, respectively. It should be noted that PSNR was calculated by comparing each frame with its reference and then taking the average for the whole video. It can be seen from these results that a better quality can be obtained by using more eigenfunctions, and the best case is by using all eigenfunctions.Figure 9
The recovered gray-scale video using the most 35 energetic eigenfunctions.


## 6.2. Transmission of a Color Video

In this subsection, we consider the transmission of a color video using the developed communication scheme. The color version of the traffic video used in Section6.1 is utilized here. Figure 10 presents the frames of the color video which are represented by a 120×160×3 data matrices.Figure 10
The frames of the original color video to be transmitted using the proposed scheme.First, K-L decomposition is applied on these 49 frames to obtain 49 eigenfunctions. Figure11 depicts the most 16 energetic eigenfunctions. The generated 16 most energetic eigenfunctions are transmitted using a public channel. Figure 12 depicts the energy associated with each eigenfunctions. Figure 12(a) shows the energy of all eigenfunctions while Figure 12(b) depicts the energy associated with the rest of the eigenfunctions not including the first one.Figure 11
The most 16 energetic eigenfunctions.Figure 12
Energy associated with the eigenfunctions (color video).
(a) (b)Next, we transform the obtained data coefficients into binary to form a sequence of pulses as described in Section6.1. This sequence of pulses is added to the ym state of the master system and all the states of the system are masked and transmitted. The sliding mode control law at the receiver end is used to synchronize the master and slave systems; see Figures 13 and 14. A noisy version of the transmitted data is then recovered by using ey where a part of that message is shown in Figure 15.Figure 13
The errors of the Chen systems versus time for 50 seconds (color video).Figure 14
The errors of the unified chaotic systems versus time for 50 seconds (color video).Figure 15
The recovered signalmc(t) versus time for 100 seconds (color video).Once the sent message is recovered, it is transformed back from binary to obtain the real-valued data coefficients. The recovered data coefficients are used in conjunction with the received eigenfunctions to reconstruct the data vectors using (7). Finally, the data vectors are reshaped to retrieve the transmitted color frames. The retrieved frames using all eigenfunctions which proves the efficiency of the developed communication scheme are presented in Figure 16.Figure 16
The recovered video using all eigenfunctions (color video).The reconstructed frames when only the most 35 energetic eigenfunctions are used are shown in Figure17. Moreover, the PSNR was calculated as a quality metric by considering different cases using 49, 35, 30, and 15 most energetic eigenfunctions for transmission and reconstruction. The corresponding PSNR values are 56.7 dB, 41.8 dB, 37.4 dB, and 27.5 dB, respectively. It is obvious that the reduction of transmitted eigenfunctions and coefficients comes at the expense of the reconstructed video quality which is a trade-off that can be made depending the desired requirements.Figure 17
The recovered video using the most 35 energetic eigenfunctions (color video).


## 7. Conclusion

This article considers a unique secure communication scheme developed by using the Karhunen-Loéve decomposition and the synchronization of master with slave systems where each system consists of the Chen hyperchaotic system and the unified chaotic system is considered. The Karhunen-Loéve decomposition is employed as a data reduction method to successfully generate data coefficients and optimal eigenfunctions that capture the original set of video frames. Using only the most energetic eigenfunctions to approximate the original frames leads to computational savings. The obtained data coefficients are encrypted before being transmitted using a master system and received using a slave system through the secure communication scheme. A sliding mode control design is presented to synchronize the master and slave systems. Furthermore, computer simulations are executed to verify the performance of the proposed scheme, and the results of these simulations are presented and discussed.The real-time implementation of the proposed method is a challenging task and will be the subject of future research studies.



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*Source: 6382532-2018-10-31.xml*