Improved Tian's Method for Medical Image Reversible ... - CiteSeerX

GVIP Journal, Volume 7, Issue 2, August, 2007

Improved Tian’s Method for Medical Image Reversible Watermarking

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Imen Fourati Kallel1,2, Mohamed Salim Bouhlel1 and Jean-Christophe Lapayre2 RU: Sciences and Technologies of Image and Telecommunications (SETIT),ISBS - Tunisia 2 Laboratory of Franche Comte (LIFC) (FRE CNRS 2661) Besançon – France

[email protected], [email protected], [email protected]

Abstract

2. Features of reversible watermarking systems

Fragile watermarking techniques ensured content authentication and tamper proofing but the permanent embedding distortion is intolerable in the medical field even it is imperceptible. The most adequate solution for this problem is the reversible watermarking. In this paper, we started by giving a short overview of some reversible watermarking techniques to pass thereafter to present an improvement of the Tian’s method.

• Transparency: The watermark should be invisible to human perception after being embedded into the host image so as to minimise the impact on the perceptual quality. • Reversibility: The original host image is perfectly recovered after the marked image passes the authentication process.. • Fragility: The embedded watermark is expected to be destroyed when the attacks are mounted on the host image. • Blind extraction: The extraction process does not need the original image

Key words: content authentication, medical field, reversible watermarking.

1. Introduction In the last few years, fragile watermarking [1] has been widely used to authentication and content integrity verification. This technique modify the host image in order to insert the pattern but the permanent embedding distortion is intolerable for the applications that require high quality images such as medical and military images.The most adequate solution for this problem is the reversible watermarking . The reversible watermarking [2] not only provides authentication and tamper proofing [3] but also can recover the original image from the suspected image. After the verification process if the transmitted image is deemed to be authentic the doctor reconstitutes the original image (without any degradation) and uses it in its diagnosis avoiding all risk of modification. In this paper, after achieve the essential features of reversible watermarking systems in section 2; we will give in section 3 a short overview of some reversible watermarking techniques. Then we present in section 4 an improved version of Tian’s algorithm for reversible data embedding. Finally, the experimental results are presented to validate the proposed scheme.

3. Related work In 1997 Barton [4] proposed one of the earliest reversible watermarking schemes, which losslessly compress the bits to be affected by the embedding operation to preserving the original data and creating space for the watermark. The compressed data and the watermark are then embedded into the host image. This practice of compressing original data for reversibility purpose has been widely adopted. In 2002 Fridrich [5]compresses one of the least significant bit planes of the host image, calculates the hash of the original image using MD5 algorithm, encrypts the result, and finally, replaces the original bit plane with the result. The capacity of the algorithms of Fridrich is low Celik,[6]enhanced Fridrich’s approach and devised a low distortion, reversible watermark that is capable of embedding as high as 0.7 bits/pixel. One limitation of the previously mentioned authentication schemes is that the capacity insertion depends on image structure. Tian [7] presented a new method for reversible data embedding which he called difference expansion. This method is based on Haar wavelet transform of the image. Tian divides the image into pairs of pixels then embeds one bit into the difference of the pixels of each pair from those pairs that are not expected to

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cause an overflow or underflow. The location map that indicates the modified pairs is compressed and included in the payload • In addition to the signature Tian inserts in the original image the location map for locate the expanded differences (ready to be marked); what generates a significant degradation in the image especially for the images which have a great capacity of insertion (natural images). • Tian method is unable to locate the falsification which the image can undergo however to be useful, fragile watermarking method must not only detect the presence of modification; but should also localize it. • The embedding capacity varies significantly depending on the nature of the host image; this technique cannot be applied to textured image where the capacity will be zero or very low.

the embedded pair P1'i etP1i the detector can extract the embedded bit bi ⎢ P + P1'i ⎥ Moy1i = ⎢ 1i ⎥ ⎣ 2 ⎦

4. Modification of tian’s algorithm

Pri' = Pi − Diff1i

General principle We present here the general principle: This spatial domain method divides the image into blocks and computes message authentication codes or watermarks corresponding to each line (32 blocks) and to each colon(32 blocks). These signatures are organized and inserted into the LSB parts of the expanded differences of selected pixels in each block. An integer reversible transformation is defined as

If the image signature verification step fails, the hierarchical authentication scheme determines the tampered regions. Our watermark extraction algorithm doesn’t use the original (unwatermarked image) to find the mark is called blind watermarking technique.

F: Where

⎢x + y⎥ Moy = ⎢ ⎥ ⎣ 2 ⎦ Diff = x − y

⎣ ⎦ is

Diff1i = P1i − P1i '

Diff1i' = d1i , 0 d1i ,1.........d1i ,nbi Diff1i = d1i , 0 d1i ,1.........d1i ,n

The verification phase starts with the extraction of the image signature and verification of the image data. If the image/signature pair is valid, the algorithm extracts and restores the originals pixels values, effectively reconstructing the original image.

⎢ Diff1i + 1⎥ Pri = Moy1i + ⎢ ⎥ 2 ⎣ ⎦

Row-column hash function: This technique [8] consists of computing the hash value for each line and each column of the original image. When we wishes to check the integrity of an image, we recomputed the hash values of the lines and columns of the image to be tested and we compares them with those of the original image. To locate the possible disparities, it is sufficient to identify the lines and the columns which are different.

(1)

the floor function: ⎣x ⎦ ix the largest

integer ≤ x Piet P’i two grayscale values where

Hl hl1(bl1,bl2,…bl32)

0 ≤ Pi , Pi ' ≤ 255 and bi ∈ {0,1} ⎢ P + Pi ' ⎥ 1≤ i ≤ n Moyi = ⎢ i ⎥ ⎣ 2 ⎦ Diff i = Pi − Pi ' The difference Diffi is presented into its binary resentation Diff i = d i ,0 d i ,1.........d i ,n We add bi into the binary representation of the difference Diffi.

Hc

adequate for the medical field.

hl32(bl1,bl2,…bl32) hc1(bc1,bc2,…bc32).

Diff i ' = d i , 0 d i ,1.........d i ,nbi Finally we compute the new grayscale values. ⎢ Diff1' + 1⎥ ( 2) P1i = Moyi + ⎢ ⎥ 2 ⎣ ⎦

P1'i = P1i − Diff i '

bi

hc32(bc1,bc2,…bc32)

Figure1. Row-column hash function

Underflow and overflow problem: Natural images admit high redundancy in pixels values thus the difference between the two neighbouring pixels is very small. But in an edge area or an area containing lots of activity, the difference of the pair of greyscale values could be large. This will

( 3)

Then the watermarked image is reconstructed by using the modified values. On the receiver side, from

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cause an underflow problem as greyscale value are restricted to the range [0,255][9 ] So the difference must be as small as possible to minimize the problem of underflow and overflow. We propose to present the second algorithm to ensure smaller value to the difference and the optimization of the preceding algorithm. The principle idea of this reversible watermarking algorithm is based on the difference in amplitude between a pixel and its value predicted.

embedding process. Figure 3 (a) shows the original image and (b), (c) are the watermarked images for comparison.

Original Image (a)

Embedding process ∧

Pi grey scales values and P i the predicted value

Pi

∑∑α P = ∑α i i

Watermarked Image (c) PSNR=57.25dB Optimization

Figure 3. The original and watermarked images

∧

where 0 ≤ Pi , Pi ≤ 255 and bi ∈ {0,1} ∧

Watermarked Image (b) PSNR=53.39dB Modified Tian

After embedding the watermarked, the watermarked image that is produced and the original image cannot be distinguished by the naked eye [10]. This condition is very important in the medical field.

(4 )

i

∧

Diff i = Pi − Pi

The difference Diffi is presented into its binary presentation Diff i = d i ,0 d i ,1.........d i ,n We add bi into the binary representation of the difference Diffi.

Diff i ' = d i , 0 d i ,1.........d i ,nbi Finally we compute the new greyscale value. ∧

P1i = P i + Diff1'

(5)

Extraction process From the embedded pair P1i the detector can extract the embedded bit bi and the original value. ∧ αP P1i = ∑∑ i i ∑α i

Figure4. Image quality

Diff1i = P1i − P1i '

Diff1'i = d1i , 0 d1i ,1.........d1i ,nbi

Localization Accuracy By dividing the whole image into regular blocks and verifying each block, any modification in the block will cause the block tagged as falsified so that the tamper could be localised.

bi

Diff1i = d1i , 0 d1i ,1.........d1i ,n '

∧

P1i = P i + Diff1i

( 6)

5. Experimental results In this section, some experiments are implemented to prove the efficiency of the proposed schemes; we analyze the quality of marked images, localization accuracy as well as detection efficiency. We tested 30 images that had been saved in JPEG format. The images had been acquired from the DICOM standard. We used a PIV machine with 256 Mo memory and the Windows XP operating system. Our approach is programmed in Matlab (Matlab7.0).

Original image Tampered image (a) (b)

Verification (c)

Figure5. Image quality

We add an object to the watermarked image; we duplicated a portion of the image in the middle of medical image as shown in Figure 5(b). Figure 5(c) show the white region clearly indicated where the tampering occurred. This operation is used to

Imperceptibility To evaluate the imperceptibility, we adopted PSNR as the indicator to reflect the distortion introduced by the 3

GVIP Journal, Volume 7, Issue 2, August, 2007 Table1. Simulation results

Tian modified

demonstrate the tampering localisation ability of the proposed scheme. In the new scheme, tamper detection is done on a block basis. Thus, tamper localization ability of the scheme is bounded by the block size used. Detection efficiency

Optimization

The effectiveness of the approach to tamper proofing is evaluated by introducing a measure; we call the detection efficiency Detection efficiency is defined as:

E=

N ad ∗100 Na

(7)

N ad : Number of affected and detected blocks Table 1 show that the proposed optimization was ensured the RFA reduce. In fact, this optimization was surmounted the underflow and overflow problem. In order to prove the import of the proposed optimization we compare the ratio of false alarms.

N a : Number of affected blocks We define also the ratio of missed detections RMD and the ratio of false alarms RFA as

⎛ N RMD = ⎜⎜1 − ad NA ⎝

⎞ ⎟⎟ * 100 ⎠

( 8)

RMD occur when the content was previously falsified but the affected region could not be detected. In order to show the validity of the proposed method; the watermarked images are tested for different types of attacks: forging, Histogram equalization, Speckle noise (0.01), Gaussian noise (0,01), rotation, Unsharp Filtering and compression JPEG (70). The studied attacks are completely detected thus the detection efficiency of the proposed scheme is 100%.(E=100% ;RMD=0%). The underflow and overflow problem effect alarms outside the tampered zone.

Figure 6.False alarms ratio

Ratio of false alarms is defined as:

RFA =

Nout * 100 N

The proposed optimization reduces the ratio of false alarms (RFA). (9)

6. Conclusion Reversible watermarking is suitable for medical images, because this kind of media do not allow any losses. In this paper, we presented reversible watermarking techniques. The presented techniques not only provide authentication and tamper proofing but also can recover the original image from the suspected image.

N out : Number of affected blocks outside the tampered zone N : Number of image blocks. Tian’s modified method provides false alarm in spite of the image is authentic.

7. References [1] Imen Fourati Kallel, Mohamed Kallel, Mohamed Salim BOUHLEL, " A Secure fragile Watermarking Algorithm for medical Image Authentication in the DCT Domain", International Conference: Information and Communication Technologies: from Theory to 4


Applications, April 24-28 2006, Damascus Syria (ICTTA 06). [2] Imen FOURATI Mohamed Kallel, JeanChristophe Lapayre et Mohamed Salim Bouhlel Tatouage reversible des images médicales Communications Via Internet des documents numériques : Application: secteur médical: Conférence Internationale. MCSEAI 2006. 0709-2006, Agadir, Maroc [3] G. Coatrieux, H. Maître, B. Sankur, Y. Rolland, and R. Collorec.Relevance of watermarking in medical imaging. In Information Technology Applications in Biomedicine, EMBS International Conference, pages 250_255.IEEE, November 2000. [4] J.M.Barton,’’ Method and apparatus for embedding authentication information within digital data,’’U.S.PATENT 5 646 997.1997 [5] J.Fridrich, M. Goljan, and R.Du. Invertible authentication. In Security and Watermarking of Multimedia contents III, volume 3971, pages 197_208. SPIE Photonics West, January 2001. [6] M.U. Celik, G. Sharma, A.M. Tekalp., and E. Saber,“Reversible Data Hiding”, In Proc. of International Conference on Image Processing, Rochester, NY, USA, Vol. 2, pp. 157160,September 24, 2002 [7] J. Tian. Reversible data embedding and content authentication using difference expansion. IEEE Transaction on Circuits and systems for Video Technology, February 2003. [8] R.B. Wolfgang and E. J. Delp. A watermark for digital images. Proceedings of the 1996 International onference on Image Processing.Vol. 3, pp. 219-222, Lausanne, Switzerland, Sept. 1996. [9] J. Tian, “Reversible data embedding using a difference expansion,” IEEE Trans. on Image Processing, vol. 12, no.8, August 2000, pp. 890896. [10] S.Santhosh Baboo1 and P.Subashini " Digital Water-marking for Mobile and PDA Devices based Images" Graphics, Vision and Image processing (GVIP) 05 Conference, 19-21 December 2005, CICC, Cairo, Egypt.

is the Distributed Systems and his present research interests are medical telediagnosis and telemedicine applications. Mohamed-Salim BOUHLEL was born in Sfax (Tunisia) in December 1955. He received the engineering Diploma from the National Engineering School of Sfax (ENIS) in 1981, the DEA in Automatic and Informatic from the National Institute of Applied Sciences of Lyon in 1981, the degree of Doctor Engineer from the National Institute of Applied Sciences of Lyon in 1983. He is actually the Head of Biomedical imagery Department the Higher Institute of Biotechnology Sfax (ISBS).He has received in 1999 the golden medal with the special mention of jury in the first International Meeting of Invention, Innovation and Technology (Dubai). He was the Vice President of the Tunisian Association of the Specialists in Electronics. He is actually the Vice President of the Tunisian Association of the Experts in Imagery and President of the Tunisian Association of the Experts in Information technology and Telecommunication. He is the Editor in Chief of the International Journal of Electronic, Technology of Information and Telecommunication, Chairman of the international conference: Sciences of Electronic, Technologies of Information and Telecommunication: (SETIT 2003,SETIT 2004 ,SETIT 2005 et SETIT 2007) and member of the program committee of a lot of international conferences. In addition, he is an associate professor at the Department of Image and Information Technology in the Higher National School of Telecommunication ENST-Bretagne (France). Imen FOURATI KALLEL was born in Sfax (Tunisia) in Juin 1981. She received the engineering Diploma from the National Engineering School of Sfax (ENIS) in 2004, the DEA in electronic from the National Engineering School of Sfax (ENIS) where she is currently pursuing the Doctor degree. Currently, she is a Research member in the Research Unit Sciences and Technologies of Image and Telecommunications (SETIT) ISBS – Tunisia and in Laboratory of Franche Comte (LIFC) Besançon - France. Her research interests include digital watermarking and data hiding, multimedia authentication and image processing.

Biographies Jean-Christophe Lapayre is Professor at the Computer Science Laboratory of Franche Comte (LIFC France) since 2002. He is the Head of the Computer Science Teaching Department from Besançon and the Head of Distributed Algorithmic for Tele-applications Research Group. His General Field

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