2013 a robust multiple watermarking scheme based on the DWT x

Kĩ thuật DWT in matlab

Trang 1

2013 10th International Multi-Conference

on Systems, Signals & Devices (SSD)

Hammamet, Tunisia, March 18-21, 2013

A Robust Multiple Watermarking Scheme

Based on the DWT

Ouazzane Hana, Mahersia Hela, Hamrouni Kamel

Université de Tunis El Manar, Ecole Nationale d'Ingénieurs de Tunis LR-SITI: Signal Image et Technologies de l'Information, Tunis, Tunisia

Abstract— In this paper we make contributions to a non-blind

multiple watermarking scheme that proceeds by embedding a

binary image in the discrete wavelet transform bands of a gray

scale image Unlike the common wavelet based watermarking

techniques, the proposed scheme lies essentially on marking the

approximation and diagonal bands of the discrete wavelet

transform (DWT) of the cover image achieving a better

compromise between fidelity and robustness Experiments show

that our contributions provide the multiple watermarking

scheme with robustness to a wide variety of attacks.

Index Terms— Digital watermarking, discrete wavelet

transform, non-blind image watermarking.

I INTRODUCTION The development of communication networks and the

trivialization of image processing tools have given rise to

content security problems underscoring the need to secure

digital images from illegal modification, protect their economic

interest and ensure intellectual property

Digital image watermarking is an attractive alternative that

matches these necessities This technique consists in

embedding a permanent watermark in a cover image in such a

way that the watermarked image remains accessible to

everyone and the embedded watermark can be decoded after

the watermarked image have undergone several attacks

Besides, potential attacks can be no-malicious like compression

and image enhancement techniques or malicious like

rewatermarking and cropping [1] [2] The embedded mark can

be visible or invisible

Digital watermarking has many applications according to

the type of the watermark and the used technique In general,

visible watermarking is used to reveal ownership, invisible

robust watermarking is used for copyright protection and

organization of digital contents in archiving systems, and,

invisible fragile watermarking is used for tampering detection

Image watermarking requires usually three relevant criteria

[3]:

- Fidelity: the watermarking process should not distort the

original image to ensure its commercial value

- Robustness: the inserted mark should be detectable if the cover image has undergone some potential attacks It should be, however, difficult and complex to be detected by unauthorized people Fragile watermarks should be altered

in an irreversible way if the cover image has been modified

- Capacity: it describes the necessary amount of data to be inserted in the cover image Watermarking schemes should have high capacity

Every watermarking scheme includes an encoder, process that embeds the watermark in the cover image, and a decoder, process that detects or extracts the watermark Watermarking schemes can be distinguished according to the encoding and decoding domain Effectively, images can be represented either

in the spatial domain i.e the image pixel domain, or a transformed domain such as discrete cosine transform domain

or discrete wavelet transform domain Watermark embedding

in the spatial domain is performed by modifying the cover image pixels values Watermark embedding in a transformed domain is performed by modifying the image coefficients in this selfsame domain Watermarking schemes can be distinguished according to the watermark embedding approach:

- LSB substitution Approach: embeds the watermark by substitution of some specific least significant bits (LSB) of the cover image pixels, like the schemes [4], [5] and [6]

- Additive insertion: adds the watermark to some image components, like the schemes [7], [8] and [9]

- Statistical approach: this approach is known as Patchwork [10], it performs by pseudo randomly choosing pixels from the cover image and modifying their luminosity

- Visual approach: Texture block watermarking is a method that lies on the visual approach It uses random texture patterns in the cover image It performs by producing identical textured regions by copying a randomly chosen pattern [10]

- Quantization based watermarking: This approach uses quantization to embed the watermark in the cover image For example, the scheme proposed in [11] performs by quantizing coefficients relative to some special image edges

to embed the binary watermark bits

1

Trang 2

Watermarking schemes can be also classified according to

the decoder type There are three decoding modes:

- Non-blind decoding: requires at least the original image

- Semi-blind decoding: uses only the original watermark

- Blind decoding: extracts the watermark from the possibly

distorted image using neither the original image nor the

original watermark

In this paper, we present a new watermarking scheme based

on the DWT This transform is commonly used in digital

watermarking because of its advantages Keyvanpour and

Merrikh-Bayat propose in [11] a blind watermarking scheme

that embeds the watermark in the HL and LH sub-bands

resulting from a multilevel DWT using the quantization

approach Tao and Eskicioglu propose in [12] a non-blind

multiple watermarking scheme based on the DWT First, they

apply the first or second level DWT to the cover image The

level choice depends on the watermark size that must be equal

to each sub-band thumbnail size According to the additive

approach, they embed four copies of the binary watermark into

the LL, HL, LH and HH sub-bands They apply the IDWT to

get back to the spatial domain and obtain the watermarked

image The decoding process consists in applying the DWT

and extracting the four embedded watermark copies The four

extracted watermarks are, afterwards, compared to the original

watermark to check the watermark presence in the attacked

image For objective examination, they calculate the similarity

ratio (SR) between each extracted watermark and the original

one and admit that the highest SR value helps to identify the

most resistant sub-band for a given attack Our contributions

consist in embedding only two copies of the watermark in the

High and Low frequency sub-bands In fact, the extraction

results in [12] show that the highest SR values are always

found at the LL or HH sub-bands according to the attack type

The paper continuous as follows: In section 2 we present a

brief introduction to the two dimensional DWT and we

describe the encoding and decoding processes Section 3 is

dedicated for experimental evaluation In this section we

present our test platform and the results of the method

simulation and we compare the new scheme to some schemes

based on the DWT Finally, in section 4, we give our

observations regarding the obtained scheme simulation results

and our perspectives

II PROPOSED WATERMARKING METHOD

Every two-dimensional DWT decomposition level

produces four representations of an image: an approximation

image (LL) and three detail ones (LH, HL and HH) The

approximation image represents the image low frequencies, it

has the largest coefficient magnitudes at each level and, thus,

contains the most significant information of the image To

obtain the next level decomposition, the two dimensional DWT

is applied to the LL sub-band The detail images are called the

vertical (LH), the horizontal (HL) and the diagonal (HH)

sub-bands, they represent the mid and high frequency sub-bands

and contain information about edges and texture patterns Figure 1 shows a two level decomposition

Fig 1 Two level DWT decomposition

In the following, we describe the embedding and the extraction process

A Watermark embedding process

The cover image (I) is a gray scale image We suppose that the cover image size is: N*N, then the binary watermark image (W) size must be ; n is the decomposition level during the embedding process

1 Decompose I using the n-level two dimensional DWT

2 Inserting the watermark in the LLn and HHn sub-bands by modifying their coefficients as follows :

( ) ( ) denotes the sub-band LLn or HHn

is the watermarked LLn or HHn image representation denotes the scaling factor corresponding to each sub-band Effectively, we don’t use the same scaling factor for the LLn and HHn sub-bands since the coefficient sizes are not of the same magnitude order

3 Apply the n-level IDWT to obtain the watermarked image

Î in the spatial domain

B Watermark extraction process

Let I’ be the possibly corrupted image

1 Decompose I’ using the n-level two dimensional DWT

2 Extracting the watermark from the LLn and HHn sub-bands as follows :

( ) ( ( ) ( ))⁄

is the extracted watermark from the LLn or HHn sub-bands

3 Convert to a binary image applying a simple thresholding :

2

Trang 3

proposed scheme vs PSNR = 42.230 db with Tao and

- Second level decomposition PSNR = 42.701 db with

proposed scheme vs PSNR = 42.400 db with Tao and

- First level decomposition: PSNR = 42.724 db with

the Goldhill image gives PSNR values exceeding slightly

the “BC” binary logo, used in Tao and Eskicioglu’s paper, in

III EXPERIMENTAL EVALUATION

In this part, we test the proposed watermarking scheme on

the 512*512 gray scale Goldhill test image and we use three

binary watermarks (see fig 2) Effectively the Goldhill test

image is used in Tao and Eskicioglu’s paper, thus, it will be

useful to compare the proposed scheme with Tao and

Eskicioglu’s method The tests will involve watermarking of

the LL and HH sub-bands for first and second level DWT

decomposition

To evaluate the proposed scheme fidelity, we measure the

visual quality of the watermarked image using the Peak Signal

to Noise Ratio (PSNR)

(b) Watermark used to test robustness against attacks.

(a) Goldhill cover test image

(c) Watermark used for rewatermarking attack. (d) Watermark used tocompare the proposed

scheme with Tao and Eskicioglu’s scheme.

Where, the RMSE is the square root of mean squared error

(MSE) between the original image and the distorted one

√∑ ∑ [ ( ) ( )]

Qualitative evaluation of the watermark presence can be

done by comparing the two extracted watermarks with the

original one Quantitative evaluation is performed by

calculating the similarity ratio (SR) between each extracted

watermark and the original one The SR value lies between 0

and 1

Where, S is the number of matching pixels between the

original and extracted watermarks, and D is the number of

different pixels between the same images

Figure 3 presents the PSNR values of twelve first level

watermarked images (Goldhill, Lena, Peppers, Couple,

Cameraman, Boat, F16, Barbara, Mandrill, Printer test, Zelda

Fig 2 Test platform

50 40 30 20 10 0

Fig.3 The PSNRs of twelve watermarked gray scale images each of size

512*512.

and Pirate) The figure shows that all the PSNR values are

greater than 40 db

Figure 4 presents the results of embedding the “WMK

”

(a) Watermarked image at first level decomposition, PSNR = 42.724 db.

(b) Watermarked image at first level decomposition, PSNR = 42.723 db.

binary logo into the Goldhill test image (see fig 2) Embeddin

g

PSNRs indicated in Tao and Eskicioglu’s paper :

Eskicioglu’s method

Fig 4 Watermarking results.

Fig 5 Watermark extraction results.

First level decomposition Second level decomposition

LL: SR = 1.000 HH: SR = 1.000 LL: SR = 1.000 HH: SR = 1.000

Trang 4

Figure 5 provides the extraction results without applying

any attack on the watermarked image

To evaluate the scheme robustness, we have applied

different attacks to the watermarked Goldhill image For each

attacked image, we have extracted the two embedded

watermarks and calculated the SRs Figures 6 and 7 provide the

extracted watermarks from the LL and HH sub-bands and their

appropriate SR after each attack These results suggest that:

- The LL sub-bands are most resistant to lossy

compression, filtering, geometrical deformations and

noise addition

- The HH sub-bands are robust to nonlinear deformations

of the gray scale

- Both sub-bands are resistant to rewatermarking

- Robustness is enhanced for second level decomposition

In particular, the visual quality of LL extracted

watermarks and their SR values have been visibly

increased in fig 7 for the lossy compression, low-pass

filtering, sharpening and noise addition

Comparison with previous methods

In this part, we compare the experimental results of the

proposed method with Tao and Eskicioglu’s method and

Yuan’s method [13] These two methods are based on the

multiple watermarking approach in the DWT domain Results

are shown in figures 8, 9, 10, 11, 12 and 13, they are based on

applying the same attacks on the Goldhill test image

watermarked with the same binary logo for each comparison

Figures 8, 10 and 12 provide the SR values after watermark extraction from the LL sub-band They show that the SRs of the proposed method exceed the SRs of both previous methods

In particular, the robustness of the LL sub-band has improved significantly for the gray scale deformation attacks such as histogram equalization

Figures 9, 11 and 13 provide the SR values after watermark extraction from the HH sub-band The SR values reveal also that the HH sub-band robustness is enhanced with the proposed scheme

IV CONCLUSION

In this paper, we have made a contribution to a multiple non-blind watermarking scheme based on the DWT The proposed scheme consists in applying the DWT to the gray scale cover image and modifying the LL and HH sub-band coefficients in order to insert the binary watermark according

to an additive approach

Experimental results indicate that modification of the LL and HH sub-bands results in good fidelity and robustness against a large range of attacks Watermark embedding with second level decomposition results in better robustness Objective evaluation shows that the proposed method outperforms Tao and Eskicioglu’s scheme in terms of fidelity and robustness

The proposed watermarking method can be further improved by automating the selection of the optimal thresholding parameter and appropriate scaling factor for each band

JPEG Compression Q=25 JPEG Compression Q=50 JPEG Compression Q=75 Gaussian filtring (3 * 3)

LL: SR = 0.813 HH: SR = 0.477 LL: SR = 0.899 HH: SR = 0.478 LL: SR = 0.958 HH: SR = 0.476 LL: SR = 0.879 HH: SR = 0.476

Gaussian filtring (5 * 5) Sharpening Histogram equalization Intensity Adjustment ([0 0.8][0 1])

Gamma correction (1.5) Pixelate Gaussian noise ([0 0.001]) Rescaling (512 -> 256 -> 512)

Fig 6 Extracting results for first decomposition level.

Trang 5

JPEG Compression Q=25 JPEG Compression Q=50 JPEG Compression Q=75 Gaussian filtring (3 * 3)

Gaussian filtring (5 * 5) Sharpening Histogram equalization Intensity Adjustment ([0 0.8][0 1])

Gamma correction (1.5) Pixelate Gaussian noise ([0 0.001]) Rescaling (512 -> 256 -> 512)

Fig 7 Extracting results for second decomposition level.

0,9

0,7

0,5

0,3

0,1

Tao and Eskicioglu's method

Proposed method

a b c d e f g h i j k

0,9 0,7 0,5 0,3 0,1

Tao and Eskicioglu's method Proposed method

a b c d e f

Fig 8 LL sub-band robustness comparison between Tao’s method and

the proposed method for first level decomposition (a) JPEG compression

(Q=25), (b) JPEG compression (Q=50), (c) JPEG compression (Q=75),

(d) Gaussian filtering (3*3), (e) Sharpening, (f) rescaling

(512->256->512), (g) Gaussian noise ([0 0.001]), (h) Histogram equalization, (i)

Intensity adjustment ([0 0.8][0 1]), (j) Gamma correction (1.5), (k)

Rewatermarking.

Fig 9 HH sub-band robustness comparison between Tao’s method and

the proposed method for first level decomposition (a) Histogram equalization, (b) Intensity adjustment ([0 0,8], [0 1]), (c) Gamma correction (1,5), (d) Sharpening, (e) Gaussian noise ([0 0.001]), (f) Rewatermarking.

Trang 6

0,9

0,7

0,5

0,3

0,1

Tao and Eskiciolu's

method

Proposed method

a b c d e f g h i j k

0,9 0,7 0,5 0,3 0,1

Yuan's method Proposed method

a b c d e f

Fig 10 LL sub-band robustness comparison between Tao’s method and

the proposed method for second level decomposition (a) JPEG

compression (Q=25), (b) JPEG compression (Q=50), (c) JPEG

compression (Q=75), (d) Gaussian filtering (3*3), (e) Sharpening, (f)

rescaling (512->256->512), (g) Gaussian noise ([0 0.001]), (h) Histogram

equalization, (i) Intensity adjustment ([0 0.8][0 1]), (j) Gamma correction

(1.5), (k) Rewatermarking.

0,9

0,7

0,5

Fig 13 HH sub-band robustness comparison between Yuan’s method

and the proposed method for first level decomposition (a) JPEG compression (Q=75), (b) Histogram equalization, (c) Intensity adjustment ([0 0,8], [0 1]), (d) Gamma correction (1,5), (e) Gaussian noise ([0 0.001]), (f) Cropping.

REFERENCES

[1] V M Potdar, S Han and E Chang, “A survey of digital image watermarking techniques,” Industrial Informatics, 3rd IEEE International Conference on, pp 709-716, 2005 [2] S P Mohanty, “Digital watermarking : a tutorial review”, unpublished.

[3] E Ganic and A M Eskicioglu, “Robust DWT-SVD domain

0,3

0,1

Tao and Eskicioglu's method Proposed method

a b c d e f

image watermarking : embedding data in all frequencies,” Proceedings of the 2004 workshop on Multimedia and Security, pp 166-174, 2004.

[4] P-Y Chen and H-J Lin, “A DWT based approach for image steganography,” International Journal of Applied Science and

Fig 11 HH sub-band robustness comparison between Tao’s method and

the proposed method for second level decomposition (a) Histogram

equalization, (b) Intensity adjustment ([0 0,8], [0 1]), (c) Gamma

correction (1,5), (d) Sharpening, (e) Gaussian noise ([0 0.001]), (f)

Rewatermarking.

0,9

0,7

Engineering 4, pp 275-290, 2006.

[5] A Bamatraf, R Ibrahim and M N M Salleh, “A new digital watermarking algorithm using combination of least significant bit (LSB) and inverse bit,” Journal of Computing, vol 3,2011 [6] R G Van Schyndel, A Z Tirkel and C F Osborne, “A digital watermark,” Proceedings ICIP-94., IEEE International Conference, vol 2, pp 86-90, 1994.

[7] E T Lin and E J Delp, “Spatial synchronization using watermark key structure,” Security, Steganography and Watermarking of Multimedia Contents, pp 536-547, 2004 [8] P Bas, B Roue and J-M Chassery, “Tatouage d’images

0,5

0,3 Yuan's method

Proposed method

couleur additif : vers la selection d’un espace d’insertion optimal,” Coresa03, 2003.

[9] S Rastegar, F Namazi, K Yaghmaie and A Aliabadian,

“Hybrid watermarking algorithm based on singular value

0,1

Fig 12 LL sub-band robustness comparison between Yuan’s method and

the proposed method for first level decomposition (a) JPEG compression

(Q=75), (b) Gaussian filtering (3*3), (c) rescaling (512->256->512), (d)

Gaussian noise ([0 0.001]), (e) Gamma correction (1,5), (f) Cropping.

decomposition and radon transform,” International Journal of Electronics and communication (AEÜ), pp 658-663, 2011 [10] W Bender, D Gruhl, N Morimoto and A Lu, “Techniques for data hiding,” IBM Systems Journal , vol 35, pp.313, 1996.

[11] M-R Keyvanpour and F Merrikh-Bayat, “Robust dynamic watermarking in DWT domain,” Procedia Computer Science vol 3, 2010.

[12] P Tao and A M Eskicioglu, “A robust multiple watermarking scheme in the discrete wavelet transform domain,” Internet Multimedia Management Systems V, Proceedings of SPIE, pp 133-144, October, 2004.

[13] Y Yuan, D Huang and D Liu, “An integer wavelet based multiple logo-watermarking scheme,” Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences, vol 2, pp.175-179, 2006.

Trang 7

6

Tiêu đề	A Robust Multiple Watermarking Scheme Based on the Dwt
Tác giả	Ouazzane Hana, Mahersia Hela, Hamrouni Kamel
Trường học	Université de Tunis El Manar, Ecole Nationale d'Ingénieurs de Tunis
Chuyên ngành	Signal Image et Technologies de l'Information
Thể loại	Conference Paper
Năm xuất bản	2013
Thành phố	Hammamet

Định dạng
Số trang	7
Dung lượng	419,81 KB