dct based robust reversible watermarking for geometric attack · du, ting zhang have analyzed a...

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected]

Volume 1, Issue 2, July – August 2012 ISSN 2278-6856

Volume 1, Issue 2 July-August 2012 Page 27

Abstract: Reversible watermarking algorithms are used for military security applications such as sending and receiving important map of a region. Here, we represent reversible and roust watermarking technique based on DCT, which perfectly embeds the watermark in image and also extracts the original watermark successfully, though image is affected with geometric attacks such as scaling and translation. The proposed scheme embeds data by modifying those DCT coefficients with high frequency characteristics. The modifications are done in some of the high freq coefficients value accordingly with the watermarks embedding bit. We present here embedding of bits using high frequency and middle frequencies. In few images overflow mapping is essential. In many images we need not to bother about mapping. Original image is not essential for extraction of original image. When bit 1 is embedded in the DCT block, middle and high frequency coefficients are modified, while bit 0 can be embedded without any modifications in the block coefficients. This reversible method of watermarking is also robust to geometric attack. 100% similar watermark can be recovered. In our method bits are embedded into high and middle frequency coefficients, which makes the watermark more robust. 100% similar watermark and original image can be recovered if our method is supported with average counter geometric attack. Keywords: Reversible watermarking, DCT, geometric attack, high and middle frequency coefficients, PSNR.

1. INTRODUCTION With increase in technology, usage of digital media has increased tremendously. With the increasing availability of digitally stored information and the development of new multimedia services, security questions are becoming even more urgent. The acceptance of new services depends on whether suitable techniques for the protection of the watermarks are available? The development of Internet and numerous hardware and software applications have created a need for security. In a military applications, where the hidden data is of prime importance. A map of a specific region is hidden inside a image. Though image is affected with geometric attack such as translation and scaling attack, our method

recovers 100 % similar to original map. Recently, with the quick development of multimedia and network technologies, the transmission of digital products has become more widespread. Because digital products can quite easily be copied and modified, the problem of protecting their copyrights will be seriously challenged. Digital watermarking, which is an auxiliary method for traditional encryption measures, can effectively protect the copyrights of digital products. J.R.Hemandez,M.Amado have presented DCT domain watermarking techniques for still images .They have analyzed DCT domain with frequency coefficients[1]. Yi Du, Ting Zhang have analyzed A Reversible and Fragile Watermarking Algorithm Based on DCT. They have changed high frequency coefficients as per embedding bit value. This algorithm has less embedding capacity.But this algorithm is very robust to geometric attack[2]. B. Yang, M. Schmucker, X. Niu, C. Busch, S. Sun says that Integer DCT Coefficients techniques with Histogram Modification is reversible[3]. Basic DCT algorithm is useful for reversible watermarking[5-7]. According to the differences in capacities of thwarting attacks, digital watermarking can be classified into robust watermarking, semi-fragile watermarking and fragile watermarking. Robust watermarking is quite robust to modification and mainly used to protect intellectual property of digital products. Semi-fragile watermarking is partly robust, but it’s only fragile to intended tampering[8]. Fragile watermarking, which is fragile to each kind of modification, is mainly used for authentication of integrity and reliability of digital products. In some fields, such as military, medical and legal fields, the requirements for the quality of images are rigidly demanded, i.e., any drop in image quality isn’t permitted if the embedding of the watermarks leads to the drop. A new type of watermarking technology called reversible watermarking is presented recently to meet such needs . “Reversible” means that watermarked images can be restored to original ones, which also means that watermarks can be completely reversed. Reversible watermarking has a widely used prospect, so it has caused many emphases from information scientists [2]. A

DCT Based Robust Reversible Watermarking For Geometric Attack

Mr.Navnath S. Narawade1 and Dr.Rajendra D.Kanphade2

1Research Scholar,

Dept of Electronics& Telecommunication Engg. Sant Gadgebaba Amravati University, Amravati, India

2Member IEEE and Principal

Nutan Maharashtra Institute of Engineering and Technology, Pune, India.



Volume 1, Issue 2 July-August 2012 8

reversible and fragile watermarking algorithm is proposed in this paper, which can simultaneously embed the fragile and reversible watermark in an image. Geometric attack can be easily handled by this algorithm. Experiments have shown that the algorithm is effective. Moreover the nature of digital media threatens its own viability: 1) The replication of digital products is very easy. 2) The ease of transmission and multiple uses is very worrying, too. Once a single pirate copy has been made, it becomes available to anyone who wants it, without any control of the original picture owner. 3) Eventually the plasticity of digital media is a great menace. Any user (a pirate) can modify an image at will. Digital watermarking has been widely used to protect the copyright of digital images. In order to strengthen the intellectual property right of a digital image, a trademark of the owner could be selected as a watermark and embedded into the protected image. The image that embedded the watermark is called a watermarked image. Then the watermarked image could be published, and the owner can prove the ownership of a suspected image by retrieving the watermark from the watermarked image. According to the retrieved results, we can determine the ownership of the suspected image. Generally, a practical and useful watermarking scheme has to meet the following requirements after it is embedded. 1) persistence: watermarks must persist under common image modifications (e.g., Rotationa, scaling and translation ) 2) robustness: watermarks must resist digital attacks to delete, modify or bury the watermark in another, illicit one. Ideally, robustness also entails authorized, legitimate recoverability of the watermark and the signature used to create despite intervening editing. 3) unobtrusiveness:both perceptible and imperceptible watermarks should be sufficiently unobtrusive so that they do not prevent the use and appreciation of the watermarked object for the intended purposes. 4) decodability: for some invisible watermarking applications, watermarks should be readily detectable by the proper authorities, even if imperceptible to the average observer. Such decodability without requiring the original, un-watermarked image would be necessary for efficient recovery of property and subsequent prosecution.

1.1 DCT and IDCT equation: Algorithms of digital watermarking can be classified into three categories, which are algorithms of spatial domains, transformation domains and compression domains. The discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) are widely used among the transformation algorithms of digital watermarking [6-8]. The 2D-DCT and 2D-IDCT equations are respectively as follows:

DCT:-

. where, IDCT:- f(x,y)=

where,

1.2 Implementation of DCT algorithm: Block diagram of proposed method:-

Original Image

Embedding Process

Watermarked Image

Watermark

Extraction Process

Original Image

Watermark

DCTIDCT

DCT

Figure 1 Block Diagram

In this paper, watermarks can be embedded in the DCT domain. The image with the size of M×N ( M and N both are the integral times of eight) will be divided into blocks of 8×8 pixels, and then the total number of blocks is defined as , which can be given as follows

represents the block. One way is to add zigzag scanned DCT coefficients, starting from

as shown in a table I, which makes calculations complex, and other way to arrange the coefficients in a simple linear order as shown in table II, starting from

. Linear consideration of coefficients are nothing but addition of middle and high frequency coefficients which are bottom two rows. DCT coefficients whose orders are from in each compose a set of high frequency coefficients called The number and the total value of elements




in each are respectively named .

Table 1 DCT zigzag scanning order for high frequency .

Table 2 DCT straight scanning order for high and middle

frequency.

0 200 400 600 800 1000 1200-15

-10

-5

0

5

10

15

Di

Si

Graph1: Plot of Si Versus Di before embedding

0 200 400 600 800 1000 1200-30

-20

-10

0

10

20

30

Di

Si

Graph2: Plot of Si Versus Di after embedding

Coefficients in high-frequency domains always have relatively low absolute values. We have taken a high quality image called “Lena” (size: 256×256, gray-level: 0 - 255) was divided into blocks with the size of 8×8 pixels, 4x4 pixels, 16x16 pixels so the total number of blocks is

If 4x4 blocks are used L=4096 and if it is 16x16 L=256. After DCT of each block, we obtain

of all the blocks in the image. After plotting graph of , most of the Si values are between +15 and -15. If middle frequencies are allowed then Si value increases to +20 to -20. In our linear method also we got very close to zero. So we could conclude that all the of blocks in images will fluctuate in a minor interval named where

are both quite small as shown in a graph. In our method, num1 and num2 will always be set as

in a linear scanning figure or last 16 coefficients in a zigzag scanning table.

1.3 Difference between Conventional and Reversible Approach

Figure 2: Block diagram of Conventional watermarking

method

Figure3: Block diagram of reversible watermarking method.




2. ALGORITHM 1) Read the original image. 2) Divide it into blocks of size 8*8. 3) Perform DCT operation on each block of size 8*8. 4) Take watermark to be embedded and find its binary sequence. 5) If watermarking bit is 0,then no changes to be made in 8*8 blocks Si. 6) If watermarking bit=1,then changes to be made in DCTed blocks Si.

7) Perform Inverse Discrete Cosine Transform to get the Watermarked Image. 8) Take average of watermark image and use it as a watermark key. The exact reverse process should be followed for recovery of the Original Image and the Watermark.

3. RESULT

Table 3: Result table DCT DCT with counter distortion

Embedding in High freq components

Embedding in High & Middle freq components

Embedding in High freq components

Embedding in High & Middle freq components

Sr.No. Image Attack PSNR Similarity measure of extracted watermark with original watermark

PSNR Similarity measure of extracted watermark with original watermark



1 Lena Scaling attack(1.1) 18.07 100% 24.1 100% 18.07 100% 24.1 100% Translation(10) 18.07 100% 24.1 100% 18.07 100% 24.1 100% Rotation 18.07 60.93% 24.1 60.93% 18.07 100% 24.1 100% 2 Boat Scaling attack(1.1) 22.46 100% 20.03 100% 22.46 100% 20.03 100% Translation(10) 22.46 100% 20.03 100% 22.46 100% 20.03 100%

Rotation(90) 22.46 60.54% 20.03 60.93% 22.46 100% 20.03 100%

3 Barbara Scaling attack(1.1) 9.75 100% 12.60 100% 9.75 100% 12.60 100% Translation(10) 9.75 100% 12.60 100% 9.75 100% 12.60 100% Rotation(90) 9.75 60.54% 12.60 60.93% 9.75 100% 12.60 100% 4 Brain Scaling attack(1.1) 13.23 100% 19.14 100% 13.23 100% 19.14 100% Translation(10) 13.23 100% 19.14 100% 13.23 100% 19.14 100% Rotation(90) 13.23 60.54% 19.14 60.93% 13.23 100% 19.14 100%

5 Cartoon Scaling attack(1.1) 19.50 100% 16.64 100% 19.50 100% 16.64 100% Translation(10) 19.50 100% 16.64 100% 19.50 100% 16.64 100% Rotation(90) 19.50 60.54% 16.64 60.93% 19.50 100% 16.64 100% 6 Baboon Scaling attack(1.1) 24.09 100% 25.95 100% 24.09 100% 25.95 100% Translation(10) 24.09 100% 25.95 100% 24.09 100% 25.95 100% Rotation(90) 24.09 60.54% 25.95 60.93% 24.09 100% 25.95 100% 7 C’man Scaling attack(1.1) 22.89 100% 15.54 100% 22.89 100% 15.54 100%

Translation(10) 22.89 100% 15.54 100% 22.89 100% 15.54 100%

Rotation(90) 22.89 60.54% 15.54 60.93% 22.89 100% 15.54 100% 8 veg Scaling attack(1.1) 23.57 100% 19.54 100% 23.57 100% 19.54 100% Translation(10) 23.57 100% 19.54 100% 23.57 100% 19.54 100% Rotation(90) 23.5 60.54% 19.54 60.93% 23.5 100% 19.54 100%

*For exponential attack all images are giving 100% similarity




Watermarked Image recovered original image

watermark Diff between original and recovered image

Figure 3: Figure shows(a)watermarked image(b)recovered watermarked image

(c)watermark(d)difference between original and recovered image

Figure 4: Different images on which we have implemented our algorithm and lastly a watermark

3.1 Experiment and Discussion We have performed experimentation on different images such as high quality images medical images and carton images. Above result table shows that quality of watermarked image reduces. We get less PSNR as compared to histogram shifting method. But it is comparable with IWT method. As each and every coefficients are affected with this method, the quality of watermarked image reduces. But this DCT method is robust to geometric attacks such as rotation, translation and scaling attack. Result table clearly shows that this method is 100% reversible to geometric attack. The embedding capacity of this method is also very less as compared to other methods such as chaotic neural network method, difference expansion method and RCM method. But it is quite comparable with histogram

shifting method. This method’s embedding capacity can be increased by 4x4 DCT and further increased by 2x2 DCT. But it reduces quality of watermarked image. DCT method is quite suitable for geometric attacks such as scaling and translation attack. In case of scaling attack, as every coefficient is changed but it do not affect overall Si out of [T1,T2]. As the sum Si of higher and middle frequency coefficients are not affected, a 100% similar watermark recovery is possible. But if image is attacked with rotational attack, all pixel values in image changes, which results into hardly 61% similarity between original and recovered watermark. Result table shows that this method is not supporting to rotational attack.

4. CONCLUSION AND FUTURE SCOPE When watermarked image is affected with scaling and translation attack, individual high frequency coefficients and middle frequency coefficients of DCT domain image changes, but overall addition sum( Si) do not change. Hence we got 100% similar watermark and recovered image. This method gives less PSNR than any other method as well as it gives less embedding capacity. But no any other method is suitable for geometric attack. Rotational attack and other attacks are difficult to manage in DCT method also. So our aim is to develop a algorithm for rotational attack. REFERENCES [1]J.R.Hemandez,M.Amado,”DCT domain watermarking techniques for still images as detector performance analysis and a new structure,” in IEEE Transactions on image Processing,2000,vol.9,pp.55-68. [2]Yi Du, ”, Ting Zhang,”A Reversible and Fragile Watermarking Algorithm Based on DCT”,2009,ICAICI,978-0-7695-3816-07/09-IEEE DOI 10.1109/AICI 2009.30 [3] B. Yang, M. Schmucker, X. Niu, C. Busch, S. Sun, "Reversible Image Watermarking by Histogram Modification for Integer DCT Coefficients", in IEEE .6th Workshop on Multimedia Signal Processing, pp. 143-146,Siena, Italy, Sept. 2004. [4]A. K. Jain, “Fundamentals of Digital Image Processing,” New Jersey: Prentice Hall Inc.,1989. [5]A. C. Hung and TH-Y Meng, “A Comparison of fast DCT algorithms,” Multimedia Systems, No. 5 Vol. 2, Dec 1994. [6] G. Aggarwal and D. D. Gajski, “Exploring DCT Implementations,” UC Irvine, Technical Report ICS-TR-98-10, March 1998. [7] J. F. Blinn, “What's the Deal with the DCT,” IEEE Computer Graphics and Applications,July 1993, pp.78-83.




[8]Mohammad Awrangjeb, Manzur Murshed, and Guojun Lu,”Global Geometric Distortion Correction in Images”. AUTHORS

Mr. Navnath S. Narawade is a research scholar at Electronics and Telecommunication Engg department at Sant Gadgebaba Amravati University , Amravati. He received the M.E.(Electronics and Telecommunication Engg) in 2005 from Govt. College of Engg, Pune under University of Pune,

B.E.(Electronics ) from Walchand College of Engg, Sangli under Shivaji University ,Kolhapur. His research interests are focused on image and signal processing, particularly in robust reversible watermarking.

Dr. Rajendra D. Kanphade is presently Principal of Nutan Maharashtra Institute of Engineering and Technology, Pune. He has joined SSGM College of Engineering, Shegaon in 1987. He has been Head of the Electronics Department for the period Oct 2003 to Jan 2006 and also Incharge of “VLSI & Embedded System Design

Center” of SSGMCE, Shegaon. He has completed B.E. (Electronics) degree from SGB Amravati University, Amravati in the year 1987 & M.E (Electronics) from Dr. Babasaheb Ambedkar Marathwada University, Aurangabad in the year 1993. He received Ph.D.(Electronics Engg.) degree from SGB Amravati University, Amravati. His areas of research are VLSI and Embedded system Design, Analog and Mixed Signal Design. He has completed his Ph. D degree from SGB Amravati University, Amravati. He is a member of IEEE, IETE and ISTE. He has published different papers in international journals and Conferences.

dct based robust reversible watermarking for geometric attack · du, ting zhang have analyzed a...

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