COLOR IMAGE-ADAPTIVE WATERMARKING S.A.M.Gilani1 , I.Kostopoulos2 and A.N.Skodras 1,2 1
Electronics Laboratory, University of Patras, GR-26110 Patras, Greece
[email protected],
[email protected]
2
Computer Technology Institute, 61 Riga Fereou Str, GR-26221 Patras, Greece
[email protected]
Abstract: A novel scheme for the watermarking of colour images is presented in this communication. First objective is to find the most suitable alternative to RGB color space, which is highly correlated. Colour spaces with linear relation to RGB colour space with uncorrelated components are found to be most suitable for watermarking applications. Second objective is to make the scheme adaptive to ôçå colour image. This is achieved by keeping the PSNR in a predefined quality range, while tuning the watermark strength parameter. Watermark detection is fast and blind, i.e. only the watermark generation and coefficient randomization keys are needed, and not the original image. The presented results demonstrate the robustness of the method against some common image processing attacks such as compression, scaling, uniform or gaussian noise addition, median filtering, cropping and multiple watermarking.
1.
INTRODUCTION
The International Intellectual Property Alliance (IIPA) estimates the annual loss of revenue in the U.S. motion picture industry due to piracy at USD 1.3 billion, and for the record and music industries at USD 1.7 billion [1]. Thus it is an urgent need to provide protection for multimedia distribution systems. Digital watermarking has been proposed as a valid solution to the problem of copyright protection for multimedia data. The most important characteristic of watermarking is its imperceptibility and robustness. Image watermarking techniques proposed so far can be divided into two groups according to the processing domain of the host image that the watermark is embedded in. One is to modify the intensity values of the luminance in the spatial domain [2-3]. The other is to change the image coefficients in the frequency domain [4-6]. 1.1.
Transformations
RGB colour space is highly correlated and is not suitable for watermarking applications, except of the blue channel, used by some researchers because of its low sensitivity to human perception [2]. The potential of these three channels can be exploited for the application of watermarking, by decreasing the correlation among them. In the present work the effort has been put in finding the most suitable colour model for the application of image watermarking. The employed colour transforms are chosen in such a way so as to employ all the commonly used color model families. These color models are IHS, L*a*b*, YIQ, YUV. The same set of embedding and detection procedures is applied so as to achieve the best comparison among them and to be able to decide which one of them is more appropriate for watermarking. Detection can be achieved either using one channel or, for improved robustness, using all of them. This augmented robustness is an additional advantage of the independent channel watermarking. 0-7803-7503-3/02/$17.00 ©2002 IEEE
The DCT, DWT, DFT and the DHT (Discrete Hadamard Transform) are employed in most of the watermarking techniques. Jung and Mitra have introduced the subband-DCT in 1996 [7]. It is a method that involves both the DWT and the DCT. In the present work we use the DHT instead of the DCT thus achieving better robustness for watermarking. 2.
THE ADAPTIVE WATERMARKING PROCESS In watermarking system design, the first step to be considered is the embedding of the watermark. Traditionally the watermark should not be placed in perceptually insignificant regions of the image (spatial) or its frequency spectra [4]. The reason is that many signal and geometrical processes affect these components. A watermark placed in the high frequency spectrum of an image can be easily destroyed with little degradation by direct or indirect low pass filtering. On the other hand the lowpass components of an image should not be altered for two reasons: (1) as most of the image energy is concentrated in the low frequency components, any appreciable change may cause fidelity loss, and (2) the energy of these low frequency components could be considered as noise and thus subtracted, in the case that the original image is available (escrow watermarking). But in the absence of original image (blind watermarking), the image noise creates great concern during the detection phase. One of the solutions to this problem is to apply matched filtering before correlation, thus decreasing the contribution of the original image to the correlation [8], or to select low to middle level of coefficients. In the present scheme, the watermark sequence consists of real numbers W = [ w1 , w2 ,..., wn ] generated by a pseudo random number generator with a private key K2. Each value wi is drawn from a normal distribution with N(0,1) i.e. with zero mean and variance equal to one. Generally, there is an inverse proportional relation between the length of the watermark and its strength. That is, as the altered components are increased the extent to which they must be altered decreases [4]. The length can be different for the luminance and the chrominance channels. The
reason is the precedence of the luminance information over the chrominance in the human visual system. Thus these components are commonly sub-sampled to remove psychovisual redundancy as it is done also in the JPEG and MPEG standards [9]. In anticipation to this probable loss of information and relatively less sensitivity of the chrominance components, it is better to spread the watermark in the chrominance channels more than the luminance channel. In the present algorithm the length of the watermark in the chrominance channels is ten times longer than that of the luminance channel. The watermarking algorithm is shown in Fig.1. The colour image is first transformed by means of one of the colour transforms i.e. HIS, L*a*b*, YIQ or YUV, thus decreasing the correlation among the three channels. Each channel is considered as an independent image, candidate for watermarking. Single level DWT is applied to each channel and only the lowest frequency band (LL) is selected for further processing. The next step is to decorrelate the components of this low Original Image
Color transform
DWT
DHT
Zigzag scan
Key K1 2
Pseudo Randomi zation
2 1
1 40
6 is the probability of a normally distributed random variable exceeding its mean by more than six standard deviations. Hence for a small number of images, setting the threshold at T equal to six will cause false similarity extremely rare. 3.
Figure 3. Original (left) and watermarked (right) images using YUV color space
SIMULATION RESULTS
In order to support our views about the best colour transform selection for watermarking applications, results are provided in Table1. The difference of values in the chrominance channels for each colour transform indicates the superiority of linear and uncorrelated transforms YUV, YIQ as opposed to the non-linear and correlated transforms L*a*b* and IHS. Fig.3 depicts the originals and their corresponding watermarked versions of the images "lochness" and "watch", using the proposed watermarking scheme based on YUV colour space. The proposed algorithm is tested against a variety of image processing operations including uniform noise, compression, gaussian noise, median filtering, cropping and scaling. Results are provided in Table 2 for the YUV colour transform. Adobe PhotoShop was used for the test. For comparison purposes some of the results are also depicted graphically in Fig.4 for the image "watch" the proposed scheme is also tested against StirMark [12]. If the attack is not very strong, it is possible to detect the watermark. Some of the results are presented in Table 3 for the test image "watch" and the colour space YUV. As StirMark software has different modes of application, we have used the following command to generate StirMarked Images for our tests: “StirMark –T< (Target) > ”
5. [1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
4.
CONCLUSIONS
In this paper a novel watermarking algorithm for colour images is proposed. The novelty of the algorithm is in the adaptation of the watermark in the colour channels. This adaptability gives the freedom to embed watermark with maximum strength, while keeping the PSNR in between an acceptable range. It is found that linear and uncorrelated colour transforms are most suitable for watermarking. Detection is oblivious and watermark strength is not required.
[9]
[10]
[11] [12]
REFERENCES
M. Miller, I.J.Cox, and J.Bloom, “Watermarking in the Real World : An Application to DVD,” Proc. Wksp. Multimedia and Security at ACM Multimedia 98, Bristol, U.K., Sept. 1998. M.Kutter, F Jordan, and F. Bossen, "Digital signature of Color Images Using Amplitude Modulation," in Proc. SPIE Electronic Imaging 97, Stora ge and Retrieval for Image and Video Databases V, pp. 518-526, San Jose, CA, Feb, 1997. A. Nikolaidis, I. Pitas, "A Region-Based Technique For Chaotic Image Watermarking," EUSIPCO 2000, vol. II, Tampere, Finland, Sept. 4-8, 2000. I.Cox, J.Kilian, F.Thomson Leigton and T.Shamoon, “Secure Spread Spectrum Watermarking for Multimedia”, IEEE Trans. On Image Processing, vol.6, no.12, Dec. 1997. Fotopoulos,V., Krommydas,S. and Skodras,A.N., “Gabor Transform Domain Watermarking”, Proc. IEEE Int. Conf. on Image Processing (ICIP 2001), Thessaloniki, Greece, Oct. 7-10, 2001. S.A.M.Gilani and A.N.Skodras, “DLT -Based Digital Image Watermarking”, First IEEE Balkan Conference on Signal Processing, Communications, Circuits and Systems, Istanbul, Turkey, Ju ne 2-3, 2000. S.H.Jung, S.K.Mitra, “Subband DCT: Definition, Analysis, and Applications”, IEEE Trans. Circuits and Systems for Video Technology, vol.6, no3, June 1996. G.C. Langelaar, I. Setyawan and R.L. Lagendijk, "Watermarking Digital Image and Video Data; A state of The Art Overview," IEEE Signal Processing Magazine, pp. 20-46, Sept. 2000. S. J. Sangwine, "Colour in Image Processing", Electronics & Communication Engineering Journal, pp. 211-219, Oct. 2000. S. Katzenbeisser, and A. P. Petitcolas, "Information Hiding Techniques for Steganography and Digital Watermarking," Artech House, Inc., 2000. I. J. Cox, M. L. Miller, J. A. Bloom, "Digital Watermarking", Academic Press 2002. http://www.cl.cam.ac.uk/~fapp2/watermarking/stirmark /index
TABLE 1 Evaluation of the best colour transform using different images and their corresponding similarity values PSNR(dB)
S imilarity
Image
YUV
YIQ
L*a*b*
IHS
YUV
YIQ
L*a*b*
IHS
Watch Peppers Lena Lochness
39.84 39.14 39.51 39.59
39.86 39.06 39.48 39.80
39.49 39.01 39.20 39.09
39.98 39.34 39.26 39.61
18-82-83 15-24-23 16-50-51 15-73-74
18-76-78 15-24-24 16-50-51 13-72-72
18-43-40 14-12-14 16-21-20 15-48-44
18-26-47 15-20-19 16-43-39 15-35-41
TABLE 2 Evaluation of the algorithm against different image processing distortions (in parenthesis), applying YUV colour transform Gaussian
JPEG
Median
(0.97 bpp)
Filtering
Cropping Image
Uniform Noise
Noise
(15)
(10)
18-56-57 14-23-22 15-40-42 13-50-53
18-49-48 14-22-20 15-38-40 13-46-51
Down Scaling
(53.27%)
Watch Peppers Lena Lochness
(1/2) (r=3)
12-60-58 7-14-17 7-21-35 5-47-55
15-14-12 12-9-7 12-11-14 11-16-15
5-24-24 1-7-6 3-15-16 3-21-20
15-68-70 11-20-20 13-40-41 11-59-59
TABLE 3 Evaluation of the algorithm against StirMark benchmark for the test image “watch”, applying YUV colour transform Median Filter
Shearing
JPEG
Filter
Similarity
PSNR
X:Y
Similarity
PSNR
Quality
similarity
PSNR
3x3
15-64-66
30
0:1
8-39-40
23
30
13-8-7
31
4x4
6-34-33
23
0:5
1-10-12
18
40
15-11-9
32
Linear transform
Aspect Ratio
Row/Column Removal
Linear
Similarity
PSNR
X:Y
Similarity
PSNR
R:C
similarity
PSNR
A*
5-28-28
22
1.2:1
17-77-78
32
17:5
16-74-75
30
B*
4-26-25
22
0.8:1
17-78-79
35
5:17
16-73-75
30
where A * =1.013:0.008:0.011:1.008, B* =1.007:0.010:0.010:1 66 60 54 48 42 36 30 24 18 12 6 0
60 54 48 42 36 30 24 18 12 6 0 YUV
YIQ
IHS
18 12 6 0 YIQ
IHS
(c)
Color2
YIQ
Lab
IHS
Lab
(b)
78 72 66 60 54 48 42 36 30 24 18 12 6 0
24
YUV
Color1
YUV
Lab
(a)
30
Gray
YUV
YIQ
IHS
Lab
(d)
Figure 4. Detector response for the image "watch"; (Y-axis depicts the similarity values): (a) gaussian noise, (b) cropping, (c) median filtering and (d) down scaling.
