A High Payload Video Steganography Algorithm in ...

A High Payload V Video Steganography Allgorithm in DWT Domain Based on BCH Codes (15, 11) Ramadhan J. Mstafa, IEEE Student Member

Department of Computer Science and E Engineering University of Bridgeport A Bridgeport, CT 06604, USA [email protected] Abstract—Video steganography has becomee a popular topic due to the significant growth of video data over the Internet. The performance of any steganography algorithm depends on two factors: embedding efficiency and embedding payload. In this paper, a high embedding payload of videeo steganography algorithm has been proposed based on the BCH coding. To improve the security of the algorithm, a secreet message is first encoded by , , coding. Then, it is em mbedded into the discrete wavelet transform (DWT) coefficients of video frames. As the DWT middle and high frequency region ns are considered to be less sensitive data, the secret message is em mbedded only into the middle and high frequency DWT coefficien nts. The proposed algorithm is tested under two types of videos that contain slow and fast motion objects. The results of the proposed algorithm are compared to both the Least Significant B Bit (LSB) and [1] algorithms. The results demonstrate better perrformance for the proposed algorithm than for the others. The h hiding ratio of the proposed algorithm is approximately 28%, whiich is evaluated as a high embedding payload with a minimal ttradeoff of visual quality. The robustness of the proposed algorithm was tested under various attacks. The results were consisteent. Keywords—Video Steganography; BCH Embedding Efficiency; Embedding Payload

Codes;

DWT;

I. INTRODUCTION Steganography is a process that involves hiding important information (message) inside other carrier (cover) data to protect the message from unauthorized users.. The mixed data (stego objects) will be seen by the Humann Visual System (HVS) as one piece of data because the HVS w will not be able to recognize the small change that occurs in the cover data. Message and cover data could be any type of data format such as text, audio, image, and video [2]. Fig. 1 illustrates the general block diagram of steganography concepts. The development of steganalysis tools weaakens unsecure steganography schemes and rendering them m useless. Hence, researchers have to develop secure steganogrraphy algorithms that are protected from both attackers aand steganalysis detectors. Any successful steganography system should

Khaled M. Elleithy, IE EEE Senior Member

Department of Computer Science S and Engineering University of Bridgeport Bridgeport, CT 06604, USA dgeport.edu elleithy@brid consider two main important factorrs: embedding payload and embedding efficiency [3]. First, the embedding payload iss defined as the amount of secret information that is going to o be embedded inside the cover data. The algorithm has a hig gh embedding payload if it has a large capacity for the secrett message. The embedding efficiency includes the stego visual quality, security, and nd, both a low modification robustness against attackers. Secon rate and good quality of the co over data lead to a high embedding efficiency [4]. The steganography algorithm that contains a high embedding efficiiency will reduce attacker suspicion of finding hidden data an nd will be quite difficult to detect through steganalysis tools. However, H any distortion to the cover data after the embedding process p occurs will increase the attention of attackers [5]. Thee embedding efficiency is directly affected by the security of the steganographic scheme [6]. In traditional steganographic scchemes, embedding payload and embedding efficiency are oppossite. Increasing the capacity of the secret message will decrease the quality of stego videos g efficiency. Both factors that then weakens the embedding should be considered. The deciding factors depend on the steganography algorithm and the user requirements [4]. To improve steganographic schemes, many m of the algorithms use matrix encoding and block code prrinciples such as Hamming, BCH, and Reed-Solomon codes [7]]. The contributions of this paper will provide “a state of the arrt” embedding algorithm in the frequency domain that uses error correcting codes. In bedding payload algorithm addition, this original, highly emb produces a reasonable tradeoff beetween visual quality, data payload, and robustness. The remainder of this paper is organized as follows: Section 2 presents some of the related works. Section 3 m. Section 4 explains some discusses discrete wavelet transform principles of BCH codes. Section 5 presents the embedding and extracting phases of the proposed steganography d explains the experimental algorithm. Section 6 illustrates and results. Section 7 contains the conclu usions.

Fig. 1 General Block Diagram of Steganography Algorithms

78•1-4799•6776•6115/$31.00 ©2015 IEEE

II. RELATED WORKS In 2012, Zhang et al. proposed an efficientt embedder using BCH code for steganography. The embeddder conceals the secret message into a block of cover data. The embedding process is completed by changing various cooefficients in the input block in order to make the syndrome values null. The efficient embedder improves both storagge capacity and computational time compared with oth ther algorithms. According to the system complexity, Zhhang’s algorithm improves the system complexity from exponenntial to linear [8]. In 2013, Liu et al. proposed a robust stegannography scheme using H.264/AVC compressed video strream without a distortion drift in the intra-frame. The prevenntion of the intraframe distortion drift can be achieved using the directions of the intra-frame prediction. Some blocks willl be selected as cover data for embedding the secret informatiion. This process will depend on the prediction of the intra--frame modes of neighboring blocks to avert the distortion thatt propagates from the adjacent blocks. To improve system efficiency and robustness, Liu et al. applied BCH code to thee message prior to the embedding process. Then, the encodded message is embedded into the 4x4 DCT block of quanttized coefficients with only a luminance component of the intrra-frame [9]. The luminance (brightness) component to the HVS S is chosen based on its higher sensitivity than that of the color component [10]. In 2014, Diop et al. proposed an adaptivve steganography algorithm using a linear error correcting code, referred to as the low-density parity-check (LDPC) code. The allgorithm explains how to minimize the effect of secret messagge insertion using the LDPC code. For that purpose, Diop et al. ddemonstrated that the LDPC code is a better encoding algorithhm than all other codes [11]. Previously mentioned algorithms lack thhe robustness to withstand hacker attacks. With the embedding payload, flexibility exists to increase the capacity of a secret message. This paper proposes a high embedding payload video steganography algorithm in the DWT domainn based on BCH codes. M (DWT) III. DISCRETE WAVELET TRANSFORM

DWT is a well-known method that transferrs the signal from the time domain to the frequency domain [12]. The DWT separates high, middle, and low frequencies annd its boundaries from one another, while other methods, suchh as DCT, group the various frequencies into estimated regionns. The first level of the 2D-DWT image decomposition is appplied to the cover video frame. It splits the frame into fourr sub-bands, LL (approximation), LH (horizontal), HL (verrtical), and HH (diagonal), using both a low pass filter Lo_D z and a high pass filter Hi_D z for the decomposition processs. LL is a low frequency sub-band, which is an approximatioon of the original frame reduced to a quarter of its size. The L LH, HL, and HH sub-bands are middle and high frequenciies that contain detailed information about any image. In thee second level of image decomposition, the 2D-DWT is applieed to the LL subband, producing four new sub-bands [13, 14]. In the proposed algorithm, the BCH encoder was applied to the hidden data. Then, the LH, HL, and HH coefficients werre used as cover data to embed the encoded secret message. Figg. 2 illustrates the 2D-DWT.

Fig. 2 First Level of th he 2D-DWT

The results demonstrate the firrst level of decomposition. Fig. 3 shows the second level of the decomposition process.

Fig. 3 Second Level of the 2D-D DWT Decomposition

To achieve a complete recconstruction process, the following wavelet equations must bee satisfied: _

_

_

_ _

_ ,

_

2

(1)

_

(2)

In the above equations, Lo_D z and Hi_D z represent the wavelet filter bank of the decompossition process. Furthermore, Lo_R z and Hi_R z signify the wavelet w filter bank of the reconstruction process. The follo owing equations are the transfer functions of the Haar wavelet transform filters: _ _ _ _

1 1 1 1

(3) (4) (5) (6)

IV. BCH CODES O Bose, Chaudhuri, and Hocquen nghem invented the BCH encoder. It is one of the most pow werful random cyclic code methods, which can be used for deteecting and correcting errors in a block of data. The BCH code is different from the Hamming code because BCH can correct more than one bit. A binary BCH (n, k, t) can correct erro ors of a maximum t bits for codewords of the length n , , ,…, and message length k , , ,…, . Encoded E codewords and messages can both be interpreteed as polynomials, where

, and . When m and t are any positive integers where , there will be a binary BCH code 3 and 2 with the following properties: ¾ ¾ ¾ ¾ ¾

1

2

Block codeword length Message length Maximum correctable error bits Minimum distance Parity check bits

k t

2

1

,

,…,

(8)

…

(9)

In this paper, the BCH code (15, 11, 1) is used with the following parameters: 1) is a primitive element of the GF 2 , where m=4 and n 2 1. 2) The primitive polynomial is 1

.

3) There are three minimal polynomials of [16]: ¾ ¾ ¾

M x M x M x

1 1 1

x x x

x x x

x

Step1: Input the secret message (text file). Step2: Change the bits positions of the whole secret message by key1.

The BCH codes inventors decided that the generator polynomial g(x) will be the polynomial of the lowest degree in as roots on the the Galois field GF (2), with , , , … , condition that is a primitive of GF 2 . When M x is a minimal polynomial of where 1 2 , then the least common multiple (LCM) of 2t minimal polynomials will be the generator polynomial g(x). The g(x) function and the parity-check matrix H of the BCH code [15] are described as follows: 1 … 1 … 1 … (7) . . . . . . . . . . . . . . . 1 … ,

A. Data Embedding Phase The process of embedding the secret message consists of two phases: first encoding the message using the BCH code (Step 1 to 5) and then embedding the encoded message into the cover videos (Step 6 to 13). This process can be completed by the following steps:

x

,

, and (10) (11) (12)

Step3: Convert the whole secret message to a one dimensional array (1-D).

Step4: Encode the message by using the BCH (15, 11) encoder.

Step5: XOR the encoded data, which consists of 15 bits Step6: Step7: Step8: Step9: Step10:

(11 bits of message + 4 bits of parity), with the 15 bits of random value using key2. Input the cover video stream. Convert the video sequence into a number of frames. Split each frame into the YUV color space. Apply the two dimensional DWT separately to each Y, U, and V frame component. Embed the message into the middle and high frequency coefficients (LH, HL, and HH) of each of the Y, U, and V components. _

, | , ,

0 0

(13)

0 0

(14)

,

, | , ,

0 0

(15)

,

, | , ,

, ,

|

_

,

_

|

, ,

_ _

|

, ,

_

Where , , are the Y, U, and V coefficients, and S is the encoded secret message, 000, … , 111 . E is the embedding process.

Step11: Apply the inverse two dimensional DWT on the frame components.

Step12: Rebuild the stego frames from the YUV stego components.

4) The single error correction is used and the generator polynomial will be g x M x 1 x x .

Step13: Output the stego videos, which are reconstructed

5) The minimum distance of the applied BCH code (15, 11) is more than 2.

Two keys were used in the proposed steganography algorithm; each key was predefined by the sender and receiver in both the embedding and the extracting processes. The first key (key1) is used to randomly change the position of all bits in the secret message to make the message unreadable and chaotic before encoding by the BCH. The second key (key2) is used after the encoding process; the encoded message is divided into 15-bit groups, and each group is XORed with the 15-bit numbers (the 15-bit numbers were randomly generated). One of the strengths of the proposed algorithm is the usage of the two keys, which improve the security and robustness of the proposed algorithm. The block diagram of data embedding process for the proposed steganography algorithm is shown in Fig. 4.

V. THE PROPOSED STEGANOGRAPHY ALGORITHM The proposed algorithm uses uncompressed video sequences based on the frames as still images. At the beginning, the video stream is separated into frames; each frame is converted to YCbCr color space. The reason for converting to YCbCr color space is to remove the correlation between the red, green, and blue colors. A luminance (Y) component is the brightness data, which is more sensitive to the human eye than the color (chrominance) components. Consequently, the color parts can be subsampled into the video sequences, and some insignificant data can be discarded.

from all embedded frames.

Step6: Step7: Step8: Step9: Step10: Step11:

Where , , are the distorted YUV coefficients, and is th he retrieved secret message. EX is the extracting proocess. Segment the entire enccoded message into 15-bits groups. XOR each group with w the random 15-bits numbers that were gen nerated by the same key at the sender side (key2). Decode the message by the BCH (15, 11) decoder. Produce an array from the t resulted groups. Reposition the messag ge again to the original bit order using key1 Output the secrett message as a text file.

The block diagram of the dataa extracting process of the proposed steganography algorithm is shown in Fig. 5.

Fig. 4 Block Diagram of the Data Embeddingg Process

B. Data Extracting Phase This section introduces the process oof retrieving the encoded message from the stego videos first, and then CH decoder. This decoding the encoded message using the BC process can be completed by the following stepps: Step1: Input the stego videos. Step2: Convert the stego video sequencees into a number of frames. Step3: Divide each frame into the YUV coolor space. Step4: Apply the 2D-DWT separately too each Y, U, and V component. Step5: Extract the encoded message from m the middle and high frequency coefficients (LH, HL, and HH) of each Y, U, and V component. _ , ,

|

, ,

_ , ,

|

|

|

|

(16)

0

(17)

0

, , , ,

0 0

, , , ,

_ , ,

0

, ,

|

0

(18) Fig. 5 Block Diagram of the Daata Extracting Process

VI. EXPERIMENTAL RESULTS AND DISSCUSSION In this section, the performance of the prooposed algorithm is evaluated through several experiments. T The experimental environment utilizes several variables: the covver data comprise a dataset consisting of seven video sequennces of Common Interchange Format (CIF) type; also, the format of YUV is 4:2:0. In addition, the resolution of each videoo is 352 288 , and all videos are equal in length with 150 fraames. A large text file is used as a secret message. The workk is implemented using MATLAB to test the proposed algorithm m efficiency. The results are implemented using both fast annd slow motion videos. A. Visual Quality The main challenge of using video steganoography is to hide as much data as possible without degrading tthe visual quality of the stego video. PSNR is an objective quallity measurement used to calculate the difference between the original and the stego video frames. It can be obtained by folllowing equations [17]: 10 ∑

and Container videos are changing slightly from one frame to other, as compared to other five viideos. The PSNR-U of the Coastguard video has the highest dB Bs among the group. In the second figure, the PSNR-V for all video streams has been occer videos have a better calculated; the Coastguard and So quality. In Fig. 9, the PSNR com mparison for 150 frames of each video is shown. The comparisson shows that the result of the objective quality for each of th he Akiyo, Container, Bus, and Foreman videos ranged betweeen 40-42 dBs; these videos all contain slower motion objectss while the PSNR of the Coastguard, Soccer, and Tennis viddeos change frequently over time (ranges between 35-47 dB). The T changes occur because these videos contain faster motion objects o that lead to unstable visual quality.

19 ∑

,

,

20

Where O and S denote the original and sttego YUV frame components, respectively, and m and n are the video Y-components are resolutions. In Fig. 6, the PSNR of the Y calculated for all seven videos. The results off the PSNR-Y for the Akiyo, Container, Bus, and Foreman vvideos are more stable, while in the Soccer and Tennis videoos, the quality is frequently changing. The reason for the vaarying the visual quality is because the sporting videos contaain faster motion objects than the others. Overall, the Akiyo viideo has the best visual quality.

Fig. 7 PSNR Comparisons for the U-Com mponent of All Seven Videos

Fig. 8 PSNR Comparisons for the V-Com mponent of All Seven Videos

Fig. 6 PSNR Comparisons for the Y-Components of A All Seven Videos

Figs. 7 and 8 show the PSNR of the U-coomponent and the V-component, respectively, for all seven viddeos. In the first figure, the demonstrated results of the PSNR--U for the Akiyo

Table I shows the average of thee PSNR for each Y, U, and V component for all video sequen nces. The visual quality of each part is measured by separately y averaging each of the 50 frames per video. The averages are various v and depend on both the type of videos and the speed of the t motion object.

(35.58 - 45.68 dBs). The hiding raatio can be calculated as in equation 21. A number of experiiments were conducted to compare the embedding capacity of the proposed algorithm and the embedding capacity of both the LSB algorithm and [1]. Table II shows the comparrison between the three algorithms, according to the amou unt of secret data in each frame. 100%

3

21

TABLE II. CAPACITY EMBEDDING COM MPARISON OF THE PROPOSED ALGORITHM WITH BOTH [1] AN ND THE LSB ALGORITHMS Video Resolution

176 X 144

Fig. 9 PSNR Comparisons for 150 Frames of All Seven Videos ND V COMPONENT TABLE I. THE AVERAGE PSNR FOR EACH Y, U, AN FOR ALL SEVEN VIDEOS

Akiyo

Coastguard

Container

Bus

Soccer

Foreman

Tennis

Frame Number

PSNR Y

R PSNR U

PSNR V

1-50

44.799

36.6044

43.622

51-100

44.787

36.6811

43.663

101-150

44.905

36.6211

43.692

1-50

41.126

46.1666

46.990

51-100

41.064

45.0544

46.126

101-150

40.648

44.3588

46.177

1-50

39.423

42.1322

40.653

51-100

39.365

42.1711

40.752

101-150

39.442

42.3122

40.934

1-50

37.661

41.1877

41.818

51-100

36.902

40.9166

42.563

101-150

37.830

41.7377

43.417

1-50

42.429

42.9477

44.887

51-100

40.471

42.3577

45.349

101-150

50.163

43.4077

46.079

1-50

41.206

42.4899

43.025

51-100

41.374

41.9822

42.532

101-150

41.370

41.9099

42.565

1-50

39.952

42.1944

37.921

51-100

38.278

36.1144

34.966

101-150

34.538

37.5500

37.283

B. Embedding Payload According to [18], the proposed algoriithm has a high embedding payload. The obtained hiding rattio is 28.12%. A reasonable tradeoff is noticed between thee amount of the embedded message in each video (6.12 Mbytes) and the quality

Proposed Algorithm (Bits/Frame)

[1] (Bits/Frame)

LSB Algorithm (Bits/Frame)

Y

57024

4096

25344

U

14256

Not used

6336

V

14256

Not used

6336

Y

228096

8192

101376

U

57024

Not used

25344

V

57024

Not used

25344

The proposed algorithm has improved the embedding capacity of [1] and the LSB algoritthm by approximately 41.7 and 2.2 times, respectively, withoutt visual quality degradation. Fig 10 shows the capacity embed dding improvement of the proposed algorithm. Capacity Embedding (Mbytes)

Video Sequences

352 X 288

YUV

7 Proposed Alorithm

6

Reference [1] 5

LSB Algoritm

4 3 2 1 0 Proposed Alorithm

Reference [1]

LSB Algoritm

Fig. 10 Comparison of the Proposed Algorithm A with [1] and LSB

In table III, there are five videos of both fast and slow motion objects (the Soccer, Tennis, and Coastguard have fast motion objects; the Akiyo and Container Co videos have slow motion objects). Part (a) of the tablee shows the stego frame that has the lowest PSNR and its originaal frame in each video. Part (b) of the table indicates the stego frame that has the highest h video. It can be observed PSNR and its original frame in each that the minimum and maximum PS SNR of the videos that have slow motion objects are very closee to one another, as in the Akiyo and Container videos. However, the minimum and maximum PSNR of the videos that have h fast motion objects are different in dBs range such as the Soccer, Tennis, and

Coastguard videos. Overall, the objective quality of both video types is considerable. C. Robustness To evaluate the performance of the proposed algorithm for correctly retrieving the secret message, two objective metrics have been used: 1) the Similarity Function (SF) and 2) the Bit Error Rate (BER). Both metrics are used to test whether the extracted secret message has been corrupted during communication. The SF and BER can be calculated as in the following equations [19]: ∑ ∑

∑

∑

,

, ∑

,

of cover videos with fast and slow motion objects were used. Various attacks were also conducted to verify the efficiency of the algorithm. The experimental results showed that the proposed algorithm is robust against Gaussian and impulsive noises. Furthermore, it was resistant against a median filtering attack, contrary to [1] that is not robust enough against all attacks and to the LSB algorithm which is susceptible to many attacks, as well. For future work, we would like to improve the embedding payload of the proposed algorithm with the respect of the video quality by using other techniques that operate in frequency domain. Also, we would like to conduct efficient linear block codes to enhance the security of the algorithm.

22

∑

REFERENCES

, [1]

∑

∑

,

,

100%

23 [2]

where and are the embedded and extracted secret messages, respectively, and a and b are the dimensions of the secret message array. The algorithm is tested under different types of attacks (Gaussian noise with the zero mean and variance=0.01 and 0.001, Salt & pepper noise with the density=0.01 and 0.001, and median filtering). To achieve the robustness of the algorithm, the higher SF and the lower BER must be obtained. Table IV illustrates the performance of the proposed algorithm under attacks while it retrieves the hidden data with a high SF and a low BER. D. Security Analysis Despite of the high embedding payload, the security of the proposed steganography algorithm has also improved. This is mainly because two keys had been used before the embedding process to produce the unreadable message to safeguard it against attackers. Moreover, one of the strongest error correcting methods has been applied on the secret message, the BCH (15, 11) codes. VII. CONCLUSION In this paper, a high payload video steganography algorithm in the DWT domain based on BCH codes has been proposed. The steganography algorithm decomposes the video into frames; then, it divides each frame into three components (Y, U, and V). Before the embedding process, the secret message is segmented and encoded using BCH (15, 11) codes to increase the efficiency of the algorithm. The 2D-DWT has been applied to each component; both the middle and high frequency coefficients (LH, HL, and HH) are selected for embedding the secret data. In addition, during the embedding and extracting processes, this algorithm used two keys, which improved the security of the system. The proposed algorithm has a high embedding payload. The amount of the secret data in each video is approximately 6.12 Mbytes and the HR is 28.12%. According to embedding payload, the proposed algorithm outperformed both the LSB and [1] algorithms. The visual quality of the stego videos is also high: the PSNR ranged between 35.58 - 45.68 dBs with an SF=1 and a BER=0. The efficiency of the proposed algorithm is verified through a number of experiments in which a number

[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

[14]

[15] [16]

[17]

M. A. Alavianmehr, et al., "A lossless data hiding scheme on video raw data robust against H.264/AVC compression," in Computer and Knowledge Engineering (ICCKE), 2012 2nd International eConference on, 2012, pp. 194-198. H. Yuh-Ming and J. Pei-Wun, "Two improved data hiding schemes," in Image and Signal Processing (CISP), 2011 4th International Congress on, 2011, pp. 1784-1787. R. J. Mstafa and K. M. Elleithy, "A highly secure video steganography using Hamming code (7, 4)," in Systems, Applications and Technology Conference (LISAT), 2014 IEEE Long Island, 2014, pp. 1-6. C. Chin-Chen, et al., "A High Payload Steganographic Scheme Based on (7, 4) Hamming Code for Digital Images," in Electronic Commerce and Security, 2008 International Symposium on, 2008, pp. 16-21. L. Guangjie, et al., "An Adaptive Matrix Embedding for Image Steganography," in Multimedia Information Networking and Security (MINES), 2011 Third International Conference on, 2011, pp. 642-646. W. Jyun-Jie, et al., "An embedding strategy for large payload using convolutional embedding codes," in ITS Telecommunications (ITST), 2012 12th International Conference on, 2012, pp. 365-369. R. Zhang, et al., "Fast BCH Syndrome Coding for Steganography," in Information Hiding. vol. 5806, S. Katzenbeisser and A.-R. Sadeghi, Eds., ed: Springer Berlin Heidelberg, 2009, pp. 48-58. Z. Rongyue, et al., "An Efficient Embedder for BCH Coding for Steganography," Information Theory, IEEE Transactions on, vol. 58, pp. 7272-7279, 2012. Y. Liu, et al., "A Robust Data Hiding Algorithm for H. 264/AVC Video Streams," Journal of Systems and Software, 2013. I. E. Richardson, H. 264 and MPEG-4 video compression: video coding for next-generation multimedia: John Wiley & Sons, 2004. I. Diop, et al., "Adaptive steganography scheme based on LDPC codes," in 2014 16th International Conference on Advanced Communication Technology (ICACT), 2014, pp. 162-166. E. Prasad, "High Secure Image Steganography based on Hopfield Chaotic Neural Network and Wavelet Transforms," International Journal of Computer Science & Network Security, vol. 13, 2013. G. Prabakaran and R. Bhavani, "A modified secure digital image steganography based on Discrete Wavelet Transform," in Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on, 2012, pp. 1096-1100. O. S. Faragallah, "Efficient video watermarking based on singular value decomposition in the discrete wavelet transform domain," AEU International Journal of Electronics and Communications, vol. 67, pp. 189-196, 2013. Y. Hoyoung, et al., "Area-Efficient Multimode Encoding Architecture for Long BCH Codes," Circuits and Systems II: Express Briefs, IEEE Transactions on, vol. 60, pp. 872-876, 2013. A. K. Panda, et al., "FPGA Implementation of Encoder for (15, k) Binary BCH Code Using VHDL and Performance Comparison for Multiple Error Correction Control," in Communication Systems and Network Technologies (CSNT), 2012 International Conference on, 2012, pp. 780-784. A. A. Judice, et al., "An Image High Capacity Steganographic Methods by Modified OPA Algorithm and Haar Wavelet Transform,"

International Journal of Computer Science & Netw work Security, vol. 14, 2014. [18] L. Tse-Hua and A. H. Tewfik, "A novel high-capaacity data-embedding system," Image Processing, IEEE Transactions onn, vol. 15, pp. 24312440, 2006.

marking algorithm of H.264/AVC [19] Y. He, et al., "A real-time dual waterm video stream for Video-on-Demand service," AEU - International Journal of Electronics and Communica ations, vol. 66, pp. 305-312, 2012.

MUM AND MAXIMUM PSNR FOR EACH OF THE FIVE VIDEO STREAMS TABLE III. MINIM

Minimum PSNR

Maximum PSN NR

Original Frames

S Stego frames

Original Frames

Frame No. 74 in Akiyo Video

Stego frrame PSNR 41.44 dB

Frame No. 107 of Akiyo Video

Stego frrame PSNR 40.63 dB

Frame 143rd in Container Video

Stego S frame PSNR 40.99 dB

Frame 2nd in Soccer Video

Stego frrame PSNR 41.68 dB

Frame 136th in Soccer Video

Stego S frame PSNR 47.43 dB

Frame No. 86 in Tennis Video

Stego frrame PSNR 34.99 dB

Frame No. 39 in Tennis Video

Frame No. 90 in Coastguard Video

Stego frrame PSNR 43.24 dB

Frame No. 72 in Coastguard Video

Frame 26th in Container Video

a)

Stego frames

Stego S frame PSNR 41.95 dB

Stego S frame PSNR 41.08 dB

Stego S frame PSNR 47.00 dB

b)

TABLE IV. PEERFORMANCE OF THE PROPOSED ALGORITHM UNDER ATTACKS Type of Attack

Akiyo

Bus BER %

SF

1

0

Coastguard BER %

SF

1

0

BER %

SF

1

0

Container

Foreman

BER %

SF

1

0

BER %

SF

1

0

Soccer

Tennis

BER %

SF F

1

0

BER %

SF

No attacks (Salt & 0.01 Pepper) 0.001 Density= (Gaussian 0.01 white) 0.001 Variance=

0.955

4.5

0.9655

3.5

0.945

5.5

0.975

2.5

0.965

3.5

0.92 23

7.7

0.921

1

7.9

0

0.963

3.7

0.9733

2.7

0.953

4.7

0.983

1.7

0.959

4.1

0.93 32

6.8

0.933

6.7

0.923

7.7

0.9333

6.7

0.913

8.7

0.943

5.7

0.919

8.1

0.90 02

9.8

0.901

9.9

0.909

9.1

0.9199

8.1

0.899

10.1

0.929

7.1

0.898

10.2

0.87 74

12.6

0.865

13.5

Median filtering

0.986

1.4

0.9877

1.3

0.986

1.4

0.998

0.2

0.975

2.5

0.95 59

4.1

0.961

3.9