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Watermarking capacity of digital images based on domain-specific masking effects

Watermarking Capacity of Digital Images based on Domain-Speci c Masking E ects
Ching-Yung Lin : and Shih-Fu Chang

IBM T. J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY 10532 : Department of Electrical Engineering, Columbia University, New York, NY 10027 cylin@watson.ibm.com, sfchang@ee.columbia.edu

Our objective is to nd a theoretical watermarking capacity bound of digital images based on domain-speci c masking e ects. In this paper, we rst show the capacity of private watermarking in which the power constraints are not uniform. Then, we apply several domain-speci c Human Vision System approximation models to estimate the power constraints and then show the theoretical watermarking capacity of an image in a general noisy environment. Note that we consider all pixels, watermarks and noises are discrete values, which occur in realistic cases.

3, we summarize four previous techniques in estimating domain-speci c HVS masking e ect 2, 11, 16, 17].

1.1. What kind of changes are invisible?
There has been much work in analyzing the \visibility" or \noticeability" of changes made to a digital image. We can roughly categorize them into three types. They vary in the extent of utilization of the Human Vision System (HVS) model. Works of Type I consider that the just-noticeable changes are uniform in all coe cients in a speci c domain, such as the spatial domain, frequency domain, or some transform domain. PSNR is a typical measure used in these works for assessing image quality. Works of Type II apply the human vision model to some extent. Works of Type III attempts to fully apply the HVS model to predict the visibility of changes. Human Vision System models have been studied for over 30 years. These models were explored to describe human vision mechanisms such as spatial frequencyorientation channels, dependence of sensitivity on local contrast, adaptation, masking, spatial summation, and channel summation. In the literature, the most complete result involving the elds of image processing, image science, and vision science are two HVS models proposed by Lubin 10] and Daly 6]. HVS models indicate that masking e ects have di erent in uences in di erent positions either in the spatial pixel domain, frequency domain, or frequency-orientation domain. Also, general distortions such as lossy compression, blurring, ringing, etc. do not generate uniform noises in these domains. Therefore, there are obvious limitations in using Type I analysis models to predict the visibility of changes. However, Type I analysis may be more computable and can be used to provide a generic lower bound for watermarking capacity. It is lower bound because Type I analysis usually utilizes the minimum of all the invisible change values of pixels or transform coe cients.

1. Introduction
If we ignore security issues, watermarking capacity is a ected by invisibility and robustness requirements. Visual quality, robustness, and amount of embedded information compose a three-dimensional trade-o relationship. Fixing any dimension, there exist tradeo relationships between the other two dimensions. We say a watermark scheme is robust if we can extract embedded bits with an error probability deterministically equal to or statistically approaching zero. Visual quality corresponds to the quality of watermarked image. In general, if we want to make the message bits more robust against attacks, a longer codeword or larger codeword amplitudes will be necessary. However, visual quality degradation will become more signi cant. Similarly, given a xed visual quality, there exists a trade-o between the information quantity of the embedded message and robustness. It is our objective in this chapter to nd the theoretic bounds of these trade-o curves. The remaining of this paper is as follows. In Section 1.1, we discuss three types of Human Vision System approaches. In Section 1.2, we represent the obstacles of applying previous information-theoretic methods to watermarking issues. We derive the theoretical capacity of a variant state channel in Section 2. In Section

Figure 1: Watermarking: multimedia data as a communication channel In image coding literatures, some research has applied human vision mechanism to some extent. We categorize them under the Type II approach. Works in 16, 17] include a series of approaches in designing the quantization steps for the block-based DCT coe cients or wavelet coe cients . In 16], Watson et. al. proposed a content adaptive quantization method which applies some human vision mechanisms, such as local contrast and masking. These models are used to adaptively adjust quantization steps in each 8 8 block. In 17], they designed a quantization matrix for the wavelet coe cients that was conceptually similar to the role of the Quality Factor 50 matrix in JPEG. That matrix was not content dependent. They did try to estimate a contentadaptive quantization matrix, but no experiments were shown. These works may be useful for optimizing image coding parameters. However, much of characteristics in Human Vision Models derived from rigorous vision science experiments was not utilized.

1.2. Estimating Watermark capacity based on the information theory
A general watermarking model is shown in Fig. 1. Here, a message, W , is encoded to X which is added to the source multimedia data, S. The encoding process may apply some perceptual model of S to control the formation of the watermark codeword X. The resulted watermarked image, SW , can always be considered as a summation of the source image and a watermark X. At the receiver end, this watermarked image may have su ered from some distortions, e.g., additive noise, geometric distortion, nonlinear magnitude distortion, etc. The decoder utilizes the received watermarked image, ^ ^ SW , to reconstruct message, W. In general, we call the watermarking method \private" if the decoder needs the original source image S, and \public" or \blind" if S is not required in the decoding process. Watermarking capacity refers to the amount of message bits in W that

can be reliably transmitted. Most previous works on watermarking capacity directly apply the work of Shannon 15] and Costa 3]. If the whole image has uniform watermark (or said codeword) power constraint and noise power constraint in all pixel locations, then the capacity problem of private watermarking is the same as the one solved by Shannon in his original information theory paper in 1948 15]. With the same constraints, the capacity problem of public watermarking is the same as the problem that was described by Costa in 1983 3]. In both cases, the capacity is the same. That is, P C = 1 log2 (1 + N ) (bits=sample) (1) 2 where P and N are the uniform power constraints of watermark and noise, respectively. With these uniform constraints, the image can be considered as a communication channel. Shannon also showed that the channel capacity is the same if the signal values are discrete 15]. In earlier watermarking works, public watermarking is sometimes considered as a special case of private watermarking while the power of source image is included as part of noise. Costa's paper in 1983 3] seemed to show that the capacity of public watermarking is the same as the private watermarking cases where the power of the source image can be excluded. However, it is still an open issue whether the above claim is valid. The reason is that, in Costa's work 3], the design of codewords has to depend on the source signal if we want to achieve the same capacity regardless of the existence of the source signal. In other words, codewords have to be specially designed for a speci c source signal, and the codewords need to be transmitted to the decoder as well. In addition, in Shannon's theory, the channel capacity can only be achieved when the codeword length is in nite. HVS model tells us that the power constraints of watermarks are actually not uniform. Also, general distortions do not generate uniform noises in all pixel positions. Therefore, Shannon's and Costa's theories cannot be directly applied to estimate the watermark capacity. Several works including Servetto 14] and Akansu 13], did consider the variant properties in di erent locations. They considered each pixel as an independent channel and then calculated the capacity based on the theory of Parallel Gaussian Channels (PGC) 4]. However, there are controversial issues with this treatment. Compared to the PGC model, images do not have the temporal dimension in each channel. In Shannon's channel capacity theory, the maximum information transmission rate of a noisy channel can be achieved only if the codeword length is large enough (i.e., the number of samples in each channel is long enough). But given a single image,

if we consider each pixel as a channel, then there is only one sample available in each channel and therefore it is impossible to achieve the estimated capacity. The theoretical capacity analysis of an Arbitrarily Varying Channel (AVC) 5] looks more promising for this variant state problem. However, this theory dealt with cases in which the source power constraint in time (or in space) was described statistically, which is not the case for watermarking. An image may be a varying channel, but its power constraints on watermark are determined by the HVS model, and thus are not varying stochastically. Therefore, we have to develop a new informationtheoretical framework for analyzing the watermarking capacity based on discrete-value and variant-state cases. In this paper, we investigate the watermarking capacity based on Type II approaches, domain-speci c masking e ects. We rst derive the capacity of private watermarking when that power and noise constraints are not uniform across samples, i.e., the capacity issue in a variant state channel. Then, we apply domain-speci c HVS models to estimate the power constraints of watermarks. We will apply four models: Watson's DCT perceptual adaptive quantization method, Watson's wavelet quantization table, JPEG default quantization table and Chou's JND pro le. We will show the theoretical private watermarking capacity based on these four methods. A more complete discussion on watermarking capacity is shown in 8]. Also, in bounded noise cases, watermarks can be retrieved without any error. A theoretical bound of the zero-error watermarking capacity is shown in 9].

to the coe cients during transmission. Then, the maximum capacity of these multivariant symbols is C=
p(X):E (XXT ) f (S)


I(X Y) given p(Z) (2)

where p(:) represents any probability distribution and I(: :) represents mutual information. From Eq. (2), because we can assume X and Z are independent, then I(X Y) = h(Y) ; h(YjX) = h(Y) ; h(Z) (3) where h(:) represents the di erential entropy. According to Theorem 9.6.5 in 4], 8Y 2 Rn with zero mean and covariance K = E(YYT ), the di erential entropy of Y, i.e., h(Y) satis es the following h(Y) 1 log(2 e)n jKj (4) 2 with equality i Y N (0 K) and j:j is the absolute value of the determinant. Here, this theorem is valid no matter what the range of K is. Therefore, from Eq. (3), (4) and jKj = jE(YYT )j = jE(XXT ) + E(ZZT )j, we can see that C = 1 log(2 e)n jf(S) + E(ZZT )j ; h(Z): (5) 2 where we assume f(S) is diagonal and nonnegative s.t. jE(XXT )+E(ZZ T )j jf(S)+ E(ZZ T )j. This assumption means that embedded watermark values are mutually independent. Eq. (5) is the watermarking capacity in a variant-state channel without specifying any type of noise. It is the capacity given a noise distribution. If we look at Eq. (5) and Theorem 9.6.5 in 4] again, for all types of noises, we can nd that C will be at least Cmin = 1 log(2 e)n jf(S) + E(ZZT )j 2 (6) ; 1 log(2 e)n jE(ZZT )j 2 T );1 + Ij: 1 = 2 jf(S) + E(ZZ when the noise is Gaussian distributed. If we further assume that noises are also independent in samples, then the watermarking capacity will be Cmin =
n X1

2. Capacity of a variant state channel
We consider an image as a channel with spatial-variant states, in which the power constraint of each state is determined by HVS model or masking e ect in some special domains. In this way, each coe cient is considered as an independent random variable with its own noise distribution. We will not consider a coe cient as a communication channel 13, 14] or sub-channel 7] because a channel usually indicates its reuse temporally, spatially, or in other domains. Let X1 X2 :: Xn be the changes of the coe cients in a discrete image due to watermarking. We rst assume these values are continuous, and later we will show that the capacity is the same if they are quantized to discrete values. The power constraint of these values are the masking bounds determined by the source coe cient values S1 S2 ::: Sn. We de ne a masking function f s.t. E(XXT ) f(S) where X = X1 X2 :: Xn]T and S = S1 S2 :: Sn]T . Assume the watermarked coe cient values, SW = S + X. In the receiver end, consider ^ Y = SW ; S = X + Z where Z are the noises added

Pi 2 log(1 + Ni ) i=1


where Pi and Ni are the power constraints in the i ; th coe cient, respectively. It is interesting that even though we use the multivariants to derive Eq. (7) instead of using Parallel Gaussian Channels, their results are the same in this special case.

For discrete values, we can apply Theorem 9.3.1 in 4], Level 1 Level 2 Level 3 Level 4 which shows the entropy of an n-bit quantization of a LL band 14.05 11.11 11.36 14.5 continuous random variable X is approximately h(X) + LH band 23.03 14.68 12.71 14.16 n. Because, in general, only one kind of quantization HH band 58.76 28.41 19.54 17.86 would be used, in Eq. (2), we can see that the mutual HL band 23.03 14.69 12.71 14.16 information I will be the same because n will be deleted. Therefore, the capacity shown in Eq. (7) is still valid in Table 1: The quantization factors for four-level biorthogthe discrete value case. onal 9/7 DWT coe cients suggested by Watson et. al.

3. Masking e ect in speci c domains
General human vision mechanisms show that masking e ects are decided by luminance, contrast, and orientation. Luminance masking, with its basic form of Weber's e ect, describes that the brighter the background is, the higher the luminance masking threshold will be. Detection threshold for a luminance pattern typically depends upon the mean luminance of the local image region. It is also known as light adaptation of human cortex. Contrast masking refers to the reduction in the visibility of one image component by the presence of another. This masking is strongest when both components are of the same spatial frequency, orientation, and location. Human vision mechanisms are also sharply tuned to orientations 12]. Watson el. al. applied the two properties to coefcients in several di erent domains 16, 17]. Watson's model for DCT thresholds can be summarized as follows. First, an original just-noticeable-change, called mask, is assumed to be the same in all blocks. Then, these values are all adjusted by the DC values of the blocks, called luminance masking, and by the coe cients themselves, called contrast masking. Assume the original mask values are tij i j = 0::7 in all blocks, then, for the block k, set tijk = tij ( cc00k )aT (8) 00 where c00k is the DC value of the block k and c00 is the DC value corresponding to the mean luminance of the display (c00 = 128 8 = 1024 for an 8-bit gray level representation). The parameter aT is 0:649. After luminance masking, we can then perform contrast masking to get the just-noticeable-change mask values, mijk , in the block k as

mijk = max(tijk jcijkjwij t1;wij ) (9) ijk 4. Experiments of watermarking capacity where wij is an exponent between 0 and 1. A typical em- based on domain-speci c masking e ects pirical value of wij = 0:7 for (i j) 6= (0 0) and w00 = 0. In 16], Watson also de ned a variable, just-noticeable In Fig. 2, We show the estimated watermarking cadi erences (JND), as a measurement of coe cient dis- pacity based on Watson's DCT masking model, Wat1 The meaning of JND as well as Chou's JND pro le were furtortion based on the mask and the distortion value of the coe cient. If necessary, the JNDs of coe cients can be ther discussed in 8]

combined as a single perceptual distortion metric based on Minkowski metric 16]. In 17], Watson et. al. proposed a method to estimate the mask values of wavelet coe cients. These mask values depend on the viewing environment, but are independent of content. Table 1 shows a list of recommended mask values in 17]. Chou and Li proposed a JND pro le estimation method based on the luminance masking e ect and the contrast masking e ect 2]1. This model is as follows: JND(x y) = maxff1 (bg(x y) mg(x y)) f2 (bg(x y))g (10) where f1 (bg(x y) mg(x y)) = mg(x y) (bg(x y))+ (bg(x y)) 8 T (1 ; (bg(x y)=127)0:5) + 3 (11) > 0 < y) 127 f2 (bg(x y)) = > for bg(x ; 127) + 3 (12) (bg(x y) : for bg(x y) > 127 (bg(x y)) = bg(x y) 0:0001 + 0:115 (13) (bg(x y)) = ; bg(x y) 0:01 (14) The experimental result of the parameters are, T0 = 1 3 17, = 128 , and = 2 . In this model, bg(x y) is the average background luminance, and mg(x y) is the contrast value calculated from the output of high-pass ltering at four directions. f1 and f2 model the contrast and luminance masking e ects, respectively. In JPEG standard, Quality factor 50 is recommended as invisible distortion bound. Although practical invisible distortion bounds may vary depending on viewing conditions and image content, this bound is considered valid in most cases 11].


x 10


Data Hiding Capacity of the 256x256 Lenna Image


Watson wavelet mask Watson DCT mask JPEG QF50 mask Chou mask









5 6 7 Standard Deviation of Noise




Figure 2: The estimated watermarking capacity based on four domain-speci ed masks son's wavelet coe cient quantization table, JPEG recommended quantization table (i.e., Q50) and Chou's JND pro le. These just-noticeable-change masks provide estimation of the watermarking power constraint Pi in Eq. (7). Noises are assumed to be white Gaussian with its standard deviation from the range of 1 ; 10. From Fig. 2, we can see that the theoretical watermarking capacity indicates that this image can embed tens of thousand bits in the private watermarking cases. For instance, in the case where the standard deviation of noise is equal to 5 (PSNR = 34 dB), the theoretical estimated value are 84675, 102490, 37086, and 33542 bits, in the order of Watson's DCT mask, Watson's wavelet mask, JPEG quantization table, and Chou's JND pro le, respectively.

5. Conclusion
We have calculated the capacity of private watermarking with its power constraint estimated by domain-speci c masking function. We analyzed the capacity by considering image pixels as multivariant, instead of parallel Gaussian channels. Using this model, the power constraint and noise constraint can be di erent in di erent positions, an important characteristic of human vision.

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Embedding Capacity (bits)



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