Differential energy based watermarking algorithm using wavelet tree group modulation (WTGM) and Human Visual System

全文

(1)IEICE. TRANS.. FUNDAMENTALS,. VOL.E91-A,. NO.8. AUGUST. 2008. 1961. PAPER. Special Section on Signal Processing. Differential Energy Based Watermarking Algorithm Using Wavelet Tree Group Modulation (WTGM) and Human Visual System Min-Jen. SUMMARY Wavelettree basedwatermarkingalgorithmsare usingthe waveletcoefficientenergy differencefor copyrightprotectionand ownershipverification.WTQ(WaveletTreeQuantization)algorithmis therepresentativetechniqueusing energydifferencefor watermarking.According to the cryptanalysison WTQ,the watermarkembeddedin the protected imagecan be removedsuccessfully.In thispaper,we presenta noveldifferentialenergywatermarkingalgorithmbased on the wavelettree group modulationstructure,i.e. WTGM(WaveletTreeGroupModulation).The waveletcoefficientsof hostimageare dividedintodisjointsupertrees(each super tree containingtwo sub-supertrees). The watermarkis embedded in the relativelyhigh-frequencycomponentsusingthe groupstrategysuch that energiesof sub-supertreesare close.The employmentof wavelettree structure,sum-of-subsetsandpositive/negativemodulationeffectivelyimprovethe drawbacksof the WTQ schemefor its insecurity.The integration of the HVS(HumanVisual System)for WTGMprovidesa better visual effectof thewatermarkedimage.Theexperimentalresultsdemonstratethe effectiveness of our algorithmin termsof robustnessand imperceptibi li ty. keywords: copyrightprotection,HumanVisualSystem,imagewatermarking,wavelet,wavelettree quantization 1.. Introduction. Digital media files can be easily copied and distributed without any reduction in quality. As a result, digital media files are being widely distributed on the Internet today, through both authorized and unauthorized distribution channels. Piracy is a concern when security measures are not in place to protect content. Conventional cryptographic. systems permit only valid. principals (key holders) access to encrypted data. Once such digital data are decrypted, there is no way to track their reproductions or retransmissions. Over the last decade, digital watermarking has been presented to complement cryptographic protection mechanisms. Invisible watermarks can be broadly classified into three types, i.e. robust, fragile (or semi-fragile) and captioning watermarks [1], [2]. Robust watermarks are generally used for copyright protection and ownership verification as they are robust to nearly all kinds of image processing attacks. Fragile or semi-fragile watermarks are mainly applied to content authentication and integrity attestation as they are fragile to most modifications. Captioning watermarks are usually used for side information conveyance, which are required to convey more information than robust watermarks do. Manuscript received November 12, 2007. Manuscript revised March 10, 2008. The authors are with Institute of Information Management, National Chiao Tung University, Hsing-Chu, 300 Taiwan. a) E-mail: [email protected] DOI: 10.1093/ietfec/e91-a.8.1961 Copyright (c). 2008. The. Institute. of Electronics,. TSAI†a),. Member. and. Chang-Hsing. SHEN†,. Nonmember. Cox et al. [1] proposed a global DCT-based spread spectrum approach to hide watermarks. The frequency domain of the image or sound is viewed as a communication channel, and correspondingly, the watermark is viewed as a signal that is transmitted through it. The watermark is spread over very many frequency bins so that the energy in any one bin is very small and certainly undetectable. Langelaar and Lagendijk [3] introduced the DEW (Differential Energy Watermarking) algorithm for JPEG/MPEG streams in the DCT domain. The DEW algorithm embeds label bits (the watermark) by selectively discarding high frequency DCT coefficients in certain image regions. Wang and Lin [4] introduced the philosophy of WTQ (Wavelet Tree Quantization) in the DWT domain. The wavelet coefficients are grouped into so-called super trees. The wavelet-tree-based watermarking algorithm embeds watermark bits by selectively quantizing super trees. Whether the security of watermarking algorithms can be preserved if the details about algorithms are released is always a controversial issue among watermarking researchers. However, the algorithm will be known to the attacker as it is accepted in the field of cryptology [5]. Das, Maitra and Mitra had presented a successful cryptanalysis against the DEW scheme in [5]. There is a need to analyze each of the popular watermarking algorithms individually and to check whether customized attacks can be mounted to highlight the weakness of the individual watermarking algorithm itself. In this paper, we first introduce the WTQ scheme and then explain how this watermarking algorithm can be attacked by cryptanalysis. Based on the motivation to improve the security robustness of WTQ, we present a differential energy watermarking algorithm based on the wavelet tree group modulation structure, i.e. WTGM (Wavelet Tree Group Modulation). The usage of group modulation makes the proposed watermarking algorithm robust against common signal processing attacks and results in a better detector response. With the characteristic of the wavelet tree structure throughout large spatial regions, it is more robust against geometric distortions. The employment of sum-ofsubsets makes the proposed watermarking algorithm more robust against general cryptanalysis. In addition, the consideration to the CSF (Contrast Sensitive Function) and NVF (Noise Visibility Function) of the HVS (Human Visual System) provides a better visual effect of the watermarked image. The remainder of this paper is organized as follows. In Information. and Communication Engineers.

(2) IEICE TRANS. FUNDAMENTALS,. VOL.E91-A,. NO.8 AUGUST 2008. 1962. Sect.. 2,. the. WTQ. nerability. is. WTGM. scheme. reviewed. in. watermarking. experimental. 6, respectively.. 2.. WTQ. Scheme. The. WTQ. scheme. ing. scheme.. and. is. and. we. 2.1. the. Super. the. one. WTQ. quence. of. should. to. {1,-1}.. the. 1 coefficient 16. While bed. the. of. will. be. a pair. the rent in. trees. are. is. detailed. to record. a binary. PN. watermark in Fig.. i={2,3,4} A group. has. we. and. col-. j={1,2,3}, coefficients:. from. level-3, two. tree. (a). se-. bits,. 1(a),. and 21. grouping,. and. groups. and. are. there. WTQ. pair. two. operation, for. are. 42. (b). will. pair.. with. one. super. tree. information. example,. quantize. if. the. Otherwise,. the. to em-. trees. the. For. starts. super so. recording bit.. 1, we. corresponding. all. we. watermark bit. for. is used is. grouped,. Here. quantized. watermark the. trees. a super. quantization. corresponding. 5. tree.. bits.. for. Sects.. watermark-. [4]. shown. After. super. super. seed. The. in. operations:. 4 coefficients. to become. watermark. a secret. as. level-2.. every. all. super. recording. DWT. combined in. the. details.. blind Ref.. vul-. proposed. given. based refer. watermark. level-4,. from. coefficients. of. in Ci ,j, where in Fig. 1(b).. from. randomly. tree. its. the. in. are. can. the. Before. coefficients. 4,. Tree. 4-level. groups. and. Sect.. described. explain. and. coefficients. form. is. a pair. bit,. perform. locate. briefly. scheme,. watermark. explained In. conclusions. reader. information. In. 3.. a wavelet. Interested. Group. briefly. method. results. and. is Sect.. left. of. the. cur-. super. right. tree. one. will. be. trees. will. be. quantized. In. order. transformed. to perform. quantization,. to •gbit-plane•h. form. all. super. first.. (c) 2.2. A as. Form. super. tree. shown. which Fig. ger bits. the. will 1(c),. Bit-Plane. will. be. in. Fig.. be. removed. the. pair.. If we. pair. will. be. quantized. can. get. the. error. will be. bit. we to. of. is the. same. is. the. with. found. find. the. trees. out. In. a tree-. tree. is we. information. we. know order. tree, of. In. Cryptanalysis. [6],. Das. termarking model. and. which. Maitra. algorithm by. weakness. is image. that. be. the. dependent,. on the paper. every. tested. Therefore,. techniques As. claimed. as. The. Das. WTQ. and. scheme,. existing a. and. says, •gKnowledge but. not. dependent. put found of. on. secret. every. super. destroy. can. use. removal. of. constructing. the. correlation.•h. a super tree. is. tree. obtained. watermarking. indirect. Although. is. unknown,. with. two. information. technique. through. but groups.. in. the. super. knowledge. cryptanalysis. on. identification. WTQ. of quantized. estimate. of. quantized. groups.. reference. error,. can and and. be. divided. into. non-quantized. tree. groups,. quantization. of. non-. wa3.1. cryptographic Maitra. of. groups.. bit.. WTQ. should. cryptanalysis.. cryptanalytic the. on. successful. that to. we. steps: 3.. for. the. In. in a tree-. pattern,. watermark. is enough. scope,. which. quantization. current. bits. or big-. with. both. will. the. of error.. recorded. watermark,. then. according. information. reference. have. format. scope. the. When we discarded.. the. and. bit-plane the. area. watermark. checked. the. to. gray. to extract. Thus,. to. calculating. of the. every. want. for. according. energy. WTQ,. Quantization. transformed. 1(c). than the reference in the gray area In. for. Fig. 1 Illustrations of the wavelet transform and groups of wavelet coefficients. (a) A four-level wavelet transform and its subbands. The coefficients correspond to the same spatial location are grouped together. (b) The 21 coefficients of a wavelet tree. (c) Binary representation of the coefficients in the nth super tree. The shaded area denotes the bits to be discarded.. the. Identification. of. Quantized. and. Non-quantized. Groups. out. groups, seed,. All. groups. identification.. will The. be. transformed principle. into is calculating. bit-plane the. format energy. for of. last.

(3) TSAI. and SHEN:. DIFFERENTIAL. ENERGY. BASED. WATERMARKING. ALGORITHM. 1963. rows.. In our. last. rows. this. group. simulation,. in current as. a quantized. non-quantized. group.. 3.2. Estimate. of. After. identification,. estimate. groups every. last group. First,. we. are. used.. empty,. we. Otherwise,. this. If the. bits. can. assume. group. of. 4.1. is. a. take. the. set the. of. quantized. quantization. find out that Thus, ƒÃ' is. the the. energy estimated. groups error. for of. removed reference. all in. error.. 3.3. Quantization of Non-quantized Groups. When reference error has been estimated, the set of nonquantized groups will be quantized using this estimated reference error. After this step, all groups are almost quantized. In WTQ, every watermark bit is recorded by quantizing only one tree in a pair. Making all groups quantized means making all super trees quantized because a super tree is merged with two groups. Thus, if all trees are quantized, the difference caused by quantization between two trees in a pair will be eliminated. As the difference between both trees declines, it is difficult for the detector to extract the watermark bit accurately. According to our simulation of the cryptanalysis attack for WTQ, the unquantized bitplane could be successfully identified and the last two rows could be removed. Therefore, the watermark will be removed even without the reference error estimation. Therefore, WTQ is not secure enough for digital watermarking in principle. 4.. Designs of WTGM Algorithm. There are several issues need to be addressed if the energy difference will be applied for the wavelet based watermarking scheme. The first is the choice of the tree structure. How many levels should the image to be decomposed and achieve the robustness and the scalability of the watermark? The second is how to balance the robustness and the fidelity of the image on a designed energy differential watermarking algorithm? The third is how to maximize the detector response in order to render a better performance. The fourth is the security of the watermarking algorithm, the most important issue to be addressed. To resolve those mentioned issues, the same pyramidal decomposition is applied in WTGM. In addition, the idea of sum-of-subsets [5] for selecting supertrees is adopted to securely embed the watermark. Instead of the bit plane quantization for watermark embedding, the usage of positive/negative modulation will effectively render a better detector response. Also, the consideration to the CSF and NVF of the HVS provides a better visual effect and imperceptibility. The details will be explained next.. PM (Positive Modulation) and NM (Negative Modulation). Lu, et al. [7] had analyzed the behaviors of transformed coefficients under attacks. In principle, there are four. Error. calculate. in the set, and group is almost ƒÃ'.. rows. almost. group.. Reference. we. two. are. possible types of modulations: Modu(+, +), Modu(+, -), Modu(-, +), and Modu(-, -), where Modu(+/-, -/+) denotes a positive/negative transformed coefficient modulated with a negative/positive watermark quantity. No matter whether the DCT or the wavelet domain is employed, the probabilities of occurrence of the four types of modulations are all very close to 0.25. They further classified the behaviors of attacks into two categories. The first category contains those attacks like compression and blurring, which tend to decrease the magnitudes of most of the transformed coefficients. Under these circumstances, it is hoped that every transformed coefficient can be modulated with a quantity that has different sign. The reason is that it can adapt to compression-style attacks and enables more than 50% of the modulated targets to contribute a bigger positive value to the detector response. Only Modu(+, -) and Modu(-, +) will contribute positively to the detector response. The second category contains those attacks such as sharpening and histogram equalization, which have the tendency of increasing most of the magnitudes of transformed coefficients. Only Modu(+, +) and Modu(-, -) will contribute positively to the detector response. Lu, et al. emphasized that the random modulation strategy does not help the detector response. Scenarios in the attacking process are illustrated in Fig. 2. No matter whether the positive modulation or the negative modulation is employed, the modulated wavelet coefficient can effectively resist the attack in scenario 1 and 2. However, the modulated wavelet coefficient is unable to resist the attack alone in scenario 3 if the strength of the attack is larger than that of the modulation. If a watermarking algorithm simultaneously employs the positive modulation and the negative modulation in embedding a watermark bit, it can succeed in resisting the attack in scenario 3 (as cocktail watermarking did in [7], which simultaneously embedded two watermarks in complementary roles). Since the DEW scheme and the WTQ scheme only employed the philosophy of negative modulation, the detector was unable to bring the brilliant results under any kind of attacks mentioned above. Moreover, the scheme can be easily defeated by the attacker if it only employs unilateral modulation, regardless of the positive modulation or the negative modulation. Thus, a good differential energy watermarking algorithm should take both modulated methods into account for higher detector response and better security. 4.2. Wavelet Tree Structure. We employ the same wavelet tree structure as depicted in the WTQ scheme. However, each tree can be extended to involve high-frequency components as illustrated in Fig. 4..

(4) IEICE. TRANS.. FUNDAMENTALS,. VOL.E91-A,. NO.8. AUGUST. 2008. 1964. Suppose that each watermark bit is embedded using one super tree, half of a super tree is used for PM and the other is used for NM. We use the term super tree to refer to the collection of n trees (i.e. 1 super tree=n trees). A particular super tree can be divided into two sub-super trees, each containing n/2 trees. The energy of a tree t is defined as the sum of absolute values of q-p+1 wavelet coefficients. The energies of sub-super tree A and sub-super tree B are given by: EA(p,q,n)=Σ. Σ￨θi,t￨. (1). EB(p,q,n)=Σ. Σ￨θi,t￨. (2). (a). where ƒÆi. ,t denotes q denotes the. and lation. from. p. sub-super suitable. for. judging B differ. 4.4. (b) Fig.. 2. Scenarios. Negative. in. the. attacking. modulation. •go•h. indicates. the. wavelet. process.. denotes. modulated. the. wavelet. (a). original. coefficient,. Positive. modulation.. wavelet. coefficient, •gm•h. and •ga•h. means. the. For. attacked. by. be. a collection. from. level. level. 2,. and. 64. idea. of. adopted. in. sum. to. there. The. W.. are In. For. As. to. a key. factor,. if we. to form modulate bedded.. the. can. for. [8]. and. coefficient. be. they. all. Das,. two. will. be. out. coefficients. supertrees. the. as. wi. subsets. and. of. in-. integers. that. W=31,. and. how. trees tree,. according. we. can can the. is. that. NP-. used. this. [5]. the. DEW. the. coefficients. be use. scheme. are. aggregation. in the set. grouped this. watermark. of. HVS. visibility. various. the. quency. and. variation. of grating. has. of. measurements were. spatial. contrast. is. given. on and. thresholds. of of. for by. as. periodic the. for. good of. are. the. such. thresholds. gratings by. study by. a measure pattern. given. was the. such. the. studied. sinusoidal. orientations. gratings. Contrast. luminance. signals. to determine. purpose. orientation.. a need properties. visual. performed. frequencies The. been. incorporates. thresholds. conditions.. termine. that. to. given of as. defre-. relative a sinu-. equation. (3). where Lmaxand Lminare maximal and minimal luminance of a grating. Reciprocal values of contrast thresholds express the contrast sensitivity (CS), and Mannos and Sakrison [9] originally presented a model of the contrast sensitive function (CSF) for luminance (or grayscale) images is given as follows:. then. an. Mitra of. idea. of. be. a criterion. H(f)=2.6*(0.0192+0.114*f)*e-(0.114*f)1.1 itself. a closed value of energy of a tree as an element. super. The. following:. a positive. the. problem. the. Function). there. quality. measurements. with. {24,7}.. Maitra. which. is. watermark.. formulated. vulnerability. it renders with energy. selecting. S={11,13,24,7},. the. is just. of sub-super tree A and to some small quantity ƒÂ.. images,. image. The. viewing. from. will. energy or equal. two. will. C=(Lmax-Lmin)/(Lmax+Lmin). embed be. {11,13,7},. so-called the. There. will. WTGM. sum-of-subsets. find. tree. coefficients. 1.. (weights). find. example,. resolve. grouped together If we treat the and. integers is to. problem.. method. WTGM. [5]. can. the. 3, 16. level. for. securely. subsets:. fact,. one. Any. i.e. |EA-EB|•…ƒÂ,. Sensitive. psychovisual. soidal. to. goal. two. complete. for. problem n positive. W.. level. from. sum-of-subsets WTGM. are. teger. from. S2. each. coefficients,. Selection. sum-of-subsets There. and 5.. Trees. is transformed,. wavelet. coefficients. set S1 in Sect.. Super. The. 85. 4, 4 coefficients. parameter discussed. 4.3. of. image. (Contrast. for. These a 512•~512. the than. watermarked. metrics HVS.. that. 0•…q•…84).. EA=EB,. modulation. |EA-EB|•…ƒÂ. whether by less. CSF. q (0•…p•…84, with. in the tree t, p do the modu-. to. (b). coefficient.. Suppose. to. trees. the ith wavelet coefficient coefficient number used. S,. together principle bit. to em-. where. f=√. f x2 + f y2. is. the. spatial. frequency. (4) in. cy-. cles/degree of visual angle (fx and fy are the spatial frequencies in the horizontal and vertical directions, respectively). Figure 3 depicts the CSF curve which characterizes luminance sensitivity of the HVS as a function of normalized spatial frequency. According to the CSF curve, we can see that the HVS is most sensitive to normalized spatial frequencies between 0.025 and 0.125 and less sensitive to low and high frequencies [10]. Therefore, this knowledge from CSF.

(5) TSAI and SHEN: DIFFERENTIAL. ENERGY BASED WATERMARKING. ALGORITHM. 1965. (5). βk=0.01+(7.20-rk)2/7.202. where k denotes the decomposed level and rk represents the wavelet coefficient CSF of the perceptual importance weight as Fig. 4 shows. The level 1 has the largest rate for modulation, which corresponds to high-frequency components. The level 3 has the smallest rate for modulation. Under the circumstances the sum-of-subsets is employed, the actual modulation quantity of low-frequency components will be relatively small since they have larger energies. Contrarily, the actual modulation quantity of highfrequency components will be relatively large since they have smaller energies. In our study, low-frequency comFig. 3. ponents can tolerate more common signal processing while high-frequency components can tolerate more geometric attacks. The usage of high-frequency components is pretty different from the WTQ scheme for its nature of watermarking.. Luminance CSF (Courtesy of [10]).. 4.5. NVF (Noise Visibility Function) of HVS. S. Voloshynovskiy et al. [14] presented a stochastic approach based on the computation of a NVF (Noise Visibility Function) that characterizes the local image properties and identifies texture and edge regions. This allows us to determine the optimal watermark locations and strength for the watermark embedding stage. Their argument: the channel capacity is not uniform, i.e. the noise is more visible in flat areas and less visible in regions with edges and textures. Accordingly, when the local variance is small, the image is flat, and a large enough variance indicates the presence of edges or highly texture areas. The adaptive scheme based on NVF calculated from stationary GG model is the best model in our simulation, which is defined as follows: Fig. 4 A four-level wavelet tree structure. The coefficients correspond to the same spatial location are grouped together. Each tree consists of one coefficient. from. level. 4, 4 coefficients. from. level 2, and 64 coefficients from level indicated at the center of each band.. level. 3, 16 coefficients. 1. rk(ƒÀk) values. for each. NVF(x,y)=w(x,y)/ w(x,y)+σ2I. from. level k are. where. w(x,y)=γ[η(γ)]γ/‖r(x,y)‖2-γ. variance.. η(γ)=√. (gamma can. be. used. to. develop. a. simple. [10]-[13]. is. image. independent. HVS. the. model. CSF in. the. the to. masking. discrete. method their. wavelet of. masks. which. ceptual. transforms. importance. mask. in the. five-level. 12-weight. DWT. each. CSF. the. same. are. wavelet CSF. in in. to. with. [11].. 12-weight. transform.. mask. Fig.. Figure the. weights. CSF. refers. into. of. use CSF. subband. the. square. masking. is. determined. function The. in. adequate by:. [10]. to. parameter. and. rate. which. DWT. CSF. time. the. rate ƒÀk. variance.. watermark.. Since. a better. which. For. is. most. very. CSF visual. renders. a better. 4.6. WTGM. Algorithm. We. summarize. the. ing. algorithms,. which. real. local. images,. the. close,. the. image. CSF. and. NVF. the. energy. enhance. constraints. watermark. regions. of. the. of the and. the. edge. is by. 0.3•…ƒÁ•…1.. visibility the. enhance. global. ∞0e-uus-1du. determined. combination the. retains. is. range. value. The. decrease. NVF. r(x,y). is in the PSNR. σ2I is the. Γ(s)=∫. r(x,y)=(I(x,y)-I(x,y))/ƒÐI. ƒÁ and. local. the. effectively. Huang. shown. and. different.. the. 4 illustrates. the. Even. per-. approximate. modulation. and. and. Γ(3/γ)/Γ(1/γ),. parameter. shape. is quite. CSF 3. function). shape. mean. to. according. well-designed. curve. led. the. masking. presented. method. apply. coefficients. Some CSF. to. effect,. the. detector. can of. modulation. while. strength. quality. at the in texture. same and. response.. for. subband. We. effect. use. way. wavelet. the. weight. [10]. each. the. importance.. Tang. the. domain.. weighting. perceptual. one. (6). the for. ideas. mentioned. integrate. above the. advantages. in. the. follow-. of. wavelet.

(6) IEICE. TRANS.. FUNDAMENTALS,. VOL.E91-A,. NO.8. AUGUST. 2008. 1966. tree structure, sum-of-subsets for supertree selection, positive/negative modulation for watermark embedding and the CSF and NVF of the HVS into the WTGM. To quantify the existence of the watermark, the normalized correlation coefficient (NC) will be examined in order to identify the existence of the watermark. The formula of normalized correlation coefficient is as follows:. Nw. equals. be. to. 3). The by. Huffman. the. size. would. data. Since. the. The coefficient value is within -1 and 1. The complete design of the proposed algorithm is summarized as following:. 4). For. each. watermark. a). Select. b). Choose ƒ¿.. c). If. bit. wi. (i=0. to. Nw-1). do. to. for. is. originally. denotes. the. we. ployed, 5) ƒÀk is. EA,. ith. consisting. of. n trees.. EB. ,t=ƒÆi,t*(1+ƒ¿*ƒÀk*ƒÁkx,y) i=p,...,q.. ii) ƒÆi,t=ƒÆi. for (PM. for. IfƒÀk=1. i=p,...,q.. for. (NM. for. tree. 7). A). ing. t=(n/2),...,n-1,. sub-super. The. tree. B). the. readers. could. detailed. infor-. use,. there like. PGP. [16]. that. lets. indi-. extremely. are. free. strong. en-. change. difference,. required. i.e.,. after. to enthe. modand. modification).. If. the. HVS. is em-. for the strength of the embedding parameter the. NVF. watermark. and ƒÁkx ,y=1parameter where. embedding. the. is used.. the. CSF. the. the is not. NVF. HVS. employed.. employed.. super. embedding. is not. employed.. is not of. tree. list. procedure. generator algorithm Most pseudo-random quences. ,t=ƒÆi,t*(1-ƒ¿*ƒÀk*ƒÁkx,y) and. ii) ƒÆi. for. i=p,...,q.. (NM. for. i=p,...,q.. for (PM. for. mon. t=0,...,(n/2)-1,. sub-super. ,t=ƒÆi,t*(1+ƒ¿*ƒÀk*ƒÁkx,y) and. tree. A). will. but. the. be. stored. random. is not necessary to generator algorithms. tree. B). Blum. Shub, it is very. tions.. dom. number. Pass. various. back. the. the. modulated. modified. verse. DWT. The. watermark. The. length. of. The. max. wavelet. to obtain. trees. to their. coefficients. original. posi-. through. a watermarked. are of. uniformly. these. distributed. algorithms. generators, lagged Fibonacci shift registers, generalized. t=(n/2),...,n-1,. sub-super. which classes. fore,. Arrange. 6). the. information the. data. have. dur-. number. be recorded. produce se-. else i) ƒÆi. 5). after. and ƒÁkx,y=1,. If ƒÁkx,y=1,. t=0,...,(n/2)-1,. sub-super. ,t*(1-ƒ¿*ƒÀk*ƒÁkx,y). and. is (5). If ƒÀk=1,. con-. the. need |(E'A-E'B)/(EA+EB)|•†ƒ¿(E'A. it stands the CSF. formula. then. and. d). tree. 6). (wi=-1). i) ƒÆi. super. the. keys.. fractional. energy. of. files system. with. long. the. required. ification, E'B are. and. more. decrypt. de-. encryp-. doesn't. Interest. documents. and. authenticity. encode. for. encryptions. their. cryptography. change. practical. and. inissue. Data. who. [15]. For. to encrypt. and. secret. encryption and. them.. application.. algorithms. of. of. efficiently. study. a public-key. tool. and. research. anyone. unscramble. secure. cryption. force. by. cryptography. NVF(x,y) the. read. any further. is a critical. systematically can. will. small.. communication.. data. be. available. viduals. 4) ƒ¿. secret. key. which. secure. be. compression. important. The. which. the. the. tools. the. K. without. K can. transmission. extraction.. can't. mation. is. confidentiality. they. refer. K data. guarantee. of. key. zip. to. algorithms. proper. WTGM Watermark Embedding:. or. key. bytes. comparatively. key. for. image. 4608. image. algorithms. tion. so. of. advanced. is needed. the. is. the. data. tents. 1536,. coding be. secure. watermark. cryption. generator and group them in various super trees. Each super tree should be divided in two sub-super trees such that EA=EB. Store this group information which we call the image key K.. image the. studies. of which. amount. reduced. formation,. (7). value bits. compression.. for. 1) Generate a seed by mapping a signature/text through a one-way deterministic function. Obtain a PN sequence Wof length Nw using the seed. 2) Compute wavelet coefficients of a host image. Group the coefficients to form trees. 3) Randomly arrange the trees using some pseudorandom. the. 12•~2•~1536. the. in-. image.. super. Fortuna,. and. flexible. for. generator trees. under. to. are. [17] linear. and. com-. congruential. generators, feedback shift. linear feedback registers, Blum. the. twister.. Mersenne. WTGM arrange the. principle. to apply the. any. wavelet of. Theregood. ran-. trees. into. sum-of-subsets.. WTGM Watermark Extraction. Note: 1) 2). trees.. W. under. 4 level. mark. bit. of NM,. is. a super. the. is a binary watermark. value wavelet. WTGM. is. tree. (212=4096•†1536•~2).. key. K. ing. information. needs. using for at. least. number for. one PM. and 12. of •}1.. bits the. Since. water-. each. tree. the. other. is. to. mark. each. recording. sum-of-subsets. super image. super. bits. for. of. a 512•~512. Therefore,. 12•~2•~Nw under. Nw=the. decomposition.. used. needs. sequence. of Nw=1536. embedded tree. PN. where. half. used. for super. the. image. the. order-. principle.. 1) Generate a seed by mapping a signature/text through a one-way deterministic function. Obtain a PN sequence Wof length Nw using the seed. 2) Compute wavelet coefficients of a host image. Group the coefficients to form trees. 3) Reorganize the trees using the image key K. 4) For each watermark bit wi (i=0 to Nw-1) do. If. a) Select the ith super tree consisting of n trees. b) Calculate EA and EB. c) If (EA>EB) then wi=-1..

(7) TSAI and SHEN: DIFFERENTIAL. ENERGY. BASED WATERMARKING. ALGORITHM 1967. else wi=1. 5). Compute. 6). If ƒÏ. the. is. normalized. above. the. otherwise,. 5.. mark.. Experiment. correlation ƒÏ.. threshold ƒÏT,. it does. not. Without. tion. the. watermark. W. rate. is. artifacts. exists;. ment. exist.. in the of. the. watermarked. (as. shown. Results. evaluate. the. performance. 512•~512. Lena,. bits/pixel. resolution. a four-level length the. trees. part. used. to relatively DWT) scheme. 6-85. In. p=5,. order. [4]. of. sentative WTQ meet. we. and. length. Nw=512,. a false. the. used. in. is the. CDF. 9/7. 5.1. Visual. From. this. 5,. study. filters. the. different. image. the. same.. Compared. ages. demonstrate and. the. WTGM(S2) quency. are. responds ure. Table. the. to. 6(c). 1. uses. the. of. strength. of. why. the. HVS. watermark. is. im-. so that. the. first. 4 of WTQ. coefficient. num-. to relatively. high-. all. the. (a. (b. (c. (d. shown. typically. repre-. compare. and. water-. values. the. with. is. for. the With. of NC. is chosen. as. in. watermark to be The. tree. to Lena,. same. 1.. used. the. strength). 39.8dB. 0.23. Fig. 5 Watermarked images and error images. (a) WTGM(S1) watermarked Lena image at PSNR=38.2dB. (b) Scaled error image between (a) and the original image. (c) WTGM(S2) watermarked Lena image at PSNR=38.2dB. (d) Scaled error image between (c) and the original image.. wavelet. watermarking. in WTQ.. parameter. errors. 1-21. and. will. The. embed used number. settings. 5(d),. the. (S1). in. for Lena,. Goldhill. high. fre-. image.. 6(b). scheme. the. and Peppers. (b. (c. (d. images uses. which. embed. (a water-. Lena. WTQ to. im-. different. watermark, the. im-. different.. more. Figure. 6-85. at. error. are. watermarked. the. result kept. watermarked. under. 38.2dB.. is. the. (S2). have. quality. of. subbands. coefficient. and. will. PSNR. watermarked. visual. to. the. between. WTGM(S1). values. The parameter. 5(b). image. the. setting. even. setting. settings.. PSNR. the. the. wavelet. Figs.. by. than. number. 3 and. 1.03•~10-7.. while. by. 6 shows. with. efficient. different. quality. parameter. all. intensify. example. uses. 2,. (watermark. also. another. Comparison. original. signals. the are. watermarked. Figure marking. for. 6(d)).. is. NM.. 1 (S1)). Table. of. of rate. 8. The. as. setting. in. which. two. in. ages. the. shown. quality. modulation. of. same. To. 38.7. probability. Quality. Fig.. 38.2,. obvious employ-. corresponds. PSNR. it is. of ƒ¿. The visual. larger. modula-. DWT).. same. threshold ƒÏT. positive. filters. value. since as. for. comparison,. approach.. Fig.. the. it has. the are. a rectangle.. improves even. even there. half. (level the. 2 of. since. of. Peppers. respectively,. at the. the. values. WTQ,. fair. set. used parts.. uses. 1 and. based. set. is. the. 6 trees,. Set. corresponds. algorithm tree. PSNR. Goldhill. be. WTQ. wavelet. the. for. make. of. q=20). WTGM(S2). (level. will. scheme,. p=0,. q=84). to. images. in. part. components. marked. two. which. second. (i.e.. frequency. are. into. components. watermarking,. The. others. with. apparently image,. 7. We. marked. HVS, 0.378),. employ. sequence. Parameter. (i.e.. low-frequency. for. the. region. and. the with. We. consists. divided. 1-21. images. a watermark. tree. (Watermarking. number. method,. watermarking.. and. and. are. WTGM(S1). ber. PM. proposed. Peppers. for. a super. for. the. and. used. transform. experiments. coefficient. as. are. Therefore,. are. The. of. Goldhill. wavelet. 512.. in. Figure. to the. (ƒ¿=0.177. HVS. the. portant. To. consideration small. cocorFig-. water-. images.. Fig. (a). 6. Close-ups. Original. 0.177). WTGM(S2). (c). image.. for. comparison. (b). WTGM(S1). WTGM(S2) watermarked. with. watermarked with. visual. effects. watermarked. HVS. Lena. without. (ƒ¿=2.131).. (PSNR=38.2dB).. Lena HVS. without. HVS. (ƒ¿=0.378).. (ƒ¿= (d).

(8) IEICE TRANS. FUNDAMENTALS,VOL.E91-A, NO.8 AUGUST 2008 1968. Table. 2. Watermarks. extracted. from JPEG. compressed. watermarked. images.. (a). (b). 5.2 (c) Fig.. 7. (a). WTGM(S2). Test. WTGM(S2). visibility. of embedding. watermarked watermarked. watermarked marked. on. (d). Barbara. Barbara. Fig. 8. Lena Lena. without. with. HVS. with HVS. of watermark without HVS. (PSNR=25.0dB).. HVS. (ƒ¿=1.731).. (ƒ¿=9.741).. (ƒ¿=0.872).. (d). (c). (b) WTGM(S2). WTGM(S2). water-. (ƒ¿=3.374).. (a). (b). (c). (d). Close-ups for comparison with Figs. 7(a)-(d).. quality of watermarked image will be as low as 25dB for comparison purpose. From these results, we can see that there are obvious artifacts in the regions near the shoulder in Fig. 7(a) and the foot of the table in Fig. 7(c). The HVS can effectively decrease the visibility of the watermark (as shown in Figs. 7(b) and (d)). Figures 8(a)-(d) are the closeups of the images in Figs. 7(a)-(d).. Common Image Processing Attacks. 1) JPEG CompressionAttacks In this experiment, we perform JPEG compression with different quality factors (QF) on the watermarked image. The extracted results and NC values are depicted in Table 2. From these results, we can see that the proposed algorithm is robust to JPEG compression. For all cases, the extracted watermarks are with relatively high-NC values. The result of WTGM(S1) is superior to that of WTGM(S2). Even for the case that QF is equal to 20, we can still detect the embedded watermark. Since the setting for S2 reserves the watermark in the level 1 component, JPEG intentionally removes the high frequency components which make setting S1 perform better than setting S2. Therefore, the results from Table 2 are reasonable. 2) SPIHT CompressionAttacks SPIHT (Set Partitioning in Hierarchical Trees) is an imageecompression algorithm that exploits the inherent similarities across subbands in a wavelet decomposition of an image. It implies uniform quantization and bit allocation applied after wavelet decomposition. Table 3 shows the extracted tracted NC values and corresponding PSNR values between original image and attacked image. From these results, we can see that the proposed algorithm can tolerate the incidental distortions induced by high-quality SPIHT compression. Since SPIHT first removes the high frequency components during the rate reduction, the results of WTGM(S1) is also superior to those of WTGM(S2). 3) JPEG2000 CompressionAttacks JPEG2000 [18] is a new image compression standard which has good performance in high bit rate coding. It adopts wavelet transform instead of discrete cosine transform to utilize the intersubband correlation. Table 4 shows the extracted NC values and corresponding PSNR values between original image and attacked image. Since there is no data from WTQ results under JPEG2000 attack, the results under SPIHT attack are shown for comparison purpose. From these results, we can see that the proposed WTGM al-.

(9) TSAI. and SHEN:. DIFFERENTIAL. ENERGY. BASED. WATERMARKING. ALGORITHM. 1969. Table 3 Watermarks extracted from SPIHT compressed watermarked images. (a) Lena. (b) Goldhill. (c) Peppers.. Table 5 Watermarks extracted from spatial-domain-attacked watermarked images. (a) Lena. (b) Goldhill. (c) Peppers .. (a). (b). (a). (c). Table 4 Watermarks extracted from JPEG2000 compressed watermarked images. (a) Lena. (b) Goldhill. (c) Peppers.. (b). (a). (b). (c). (c). gorithm can tolerate the incidental distortions induced by JPEG2000 compression. Since JPEG2000 first removes the high frequency components during the rate reduction, the results of WTGM(S1) is also superior to those of WTGM(S2) which has similar performance as shown in Table 3.. 4) Spatial-Domain Image Processing Attacks Several spatial-domain image processing techniques, including histogram equalization, image cropping, brightness enhancement, contrast enhancement, median filtering, Gaussian filtering, sharpening, and rescale are performed on the watermarked image. The extracted results are depicted in Table 5. For all cases, the watermark information therein can be successfully recognized. Especially for those cases of histogram equalization, Gaussian filtering and sharpening, the result of WTGM(S2) is superior to that of WTGM(S1). Except for the case of Peppers Gaussian filtered image, the proposed algorithm can outperform the.

(10) IEICE. TRANS.. FUNDAMENTALS,. VOL.E91-A,. NO.8. AUGUST. 2008. 1970. Table. 6. Watermarks. extracted. from shifted. watermarked. images.. Table 8 Watermarks extracted from multiple watermarked Lena. (b) Goldhill. (c) Peppers.. images.. (a). (a). Table. 7. Watermarks. extracted. from rotated. watermarked. images.. (b). (c). 5.4. Security. Measurement. 1) Multiple. Watermarking. For ply. one. group tector WTQ. scheme. with. relatively. high-NC. values.. the. Geometric. Attacks. or. results. of. the. PSNR. Pixel. Shifting. This cularly.. 6.. is. for image.. that. of the. the. functions.. attack. for. the. pixels. left.. attacks. as. shown of. 13 pixels. to. 12. Gold-. rate. up. to. images.. size.. and. can. and. domain.. We. 3•‹ for. that. Goldhill. can. be. image. we. is. aptree. the de8 shows. through can. fallen. see. into. mul-. that. even. 25dB,. the. detected.. removal WTQ. is. can. resist. WTGM(S2),. ues. of. of. We. perform. this. which. reduces. subbands,. images.. and. one. scheme.. embedded. watermarked rithm. than. rotate. 7. Table and. the. 9 shows. 8 bitplanes. images. are. strategies. that. Under fallen. impact. on. proposed for. 26. algo-. WTGM(S1). the. and. to. designated. the. the. which. into. used. attack. removed. respectively.. attacked. major. PSNR. val-. 29dB.. shown. and. is. described. a geometri-. can 2.5•‹. for. The. sis.. watermarked. in Table. Complexity. of. Lena. is. WTGM. with. The. complexity. also. whole. transform,. Human. Vision. System. low. from. of WTGM the. complexity. view. should. sum-of-subsets,. with. of be. CSF. Human. Vision. mathematical discussed. and. NVF. analyfor. wavelet. calculation. re-. spectively.. 7. From resist. computation. System. counter-clockwise. WTGM(S2). image. 5.5. scaled. software. is the. a small. the. the. scaling. and are. the. by. [19]. it provides. rotation. results. see. image cropping. StirMark. to 3•‹ in clockwise. extracted. we. the. image,. since. the. the. attacked. to disturb Table. attacked. results,. may. wavelet. Scaling). rotating. attack. spatial. The. and. by. on. these. attacker. same. attempt watermark.. images. From of. an. the. the. Removal. Bitplane defeat. in Taup. for. modulation. Bitplane. cir-. Apparently. a shift. and. lower. image. This. 0.25•‹. results, of. this. in the. from. directions.. Peppers. the. resist. images has. rotated. original. testing. tation. such. 2). shifting to. can. (Rotation done. the. here. these. former. is. scaling. image. Peppers. the. Attacks. adopted. cal. pixels. to resist. and. attack. to. the. by. latter.. The. image. shift. Shift). done. WTGM(S2). For. Rotation. angle,. is. unable. Lena. hill. (Circular. attacks. we. Contrarily,. pixels. 2). of. Here,. WTGM(S1) ble. Attacks. kind. watermarked. value. all,. using. technique in the embedded. the. still. to. watermarks. watermarking.. watermark 1). well-known. more. modulation or to destroy. tiple 5.3. algorithms. a roand. Suppose (high-pass) and. assume. CDF. 9/7. the for. M•†N. filters. synthesis. wavelet. filters. are. transform. The. is 4(N+M)+2. cost. h. (low-pass). and. g. Take |h|=2N, |g|=2M, of. the. and. standard could. be. algorithm speeded. for up. by.

(11) TSAI. and SHEN:. DIFFERENTIAL. ENERGY. BASED. WATERMARKING. ALGORITHM. 1971. Table 9 Watermarks extracted from bitplane-removed ages. (a) Lena. (b) Goldhill. (c) Peppers.. watermarked im-. Table 10. Summary of WTGM with WTQ schemes .. (a). (b). by. the. the. window. local. mean. is O(l2),. l(=2L+1). window. size. is. of. use. ance. NVF. since. image. width. tion. of. algorithm. wavelet. The. transform. problem. a real. sum-of-subsets. stead.. Empirical. [21]. get. such. plexity. an. CSF. weighting as. plementation tion from. about. and. the. linear-time. the. other. in the. hand,. DWT. in. the. Fig.. the. domain be. be. applied. by. the. energy. Therefore,. average. the. time. comif. R. quicksort-based. is employed associated. pre-calculated. for. to. apply. each of. well. subband CSF. can parameter. the. complexity. be pre-calculated is. decided.. by r(x,y). the. NVF,ƒÅ(ƒÁ) look-up in. Eq.. and table. (6). under. Intel. In. suitable. image. sub-. size. (we. global time. vari-. complex-. 2. loop. of. seconds. to. practical. 3.0GHz,. complete. the. for. WTGM. applications. simulation. WTGM. Pentium. conclusion,. for and. 12. amount. than l. whole. than. comfrom. the. results.. gamma while. is determined. the. WTQ. In. as. other. is. shown. we. general,. WTGM(S2). the. all. The. it provides WTGM. to. clearly. WTGM. is superior. with. be. compression. resisting. as. geometric. 10). not. the. use. quantization. to. cryptanalysis-like. remove. categories. in. with can. SPIHT. ineffective. does. and. can. and. in Table. WTGM. useful. in almost. Also,. methods.. JPEG but. watermarks not. comwhich geomet-. components-WTGM(S1). in resisting. (as. high-frequency other methods, processing,. cryptanalysis.. than. cryptanalysis,. the. WTQ. well. effect. addition,. relatively. is superior to common signal. resist as. effective. In bed. with. low-frequency. distortions.. in. WTGM. visual. as. im-. the coefficient multiplicacan be efficiently done. of. the. distortions. Therefore,. Regarding. shape. ric. more. linear-time.. function. general,. ponents-WTGM(S2) can effectively. a better. perceptual. complexity. total. (O(n2.l2+n2)=O(n2)). the. less. analysis. are. The. overall. the. variance. there. Thus,. the. larger. images.. study,. Summary. relatively. the. becomes table. This. 5.6. In to. [21].. masking. the. in-. quick-. comparisons of. and. its. and. than O(n2). this global. the. to. variance. of. on. tree. and. to. results).. related. with idea. based. Therefore,. in WTGM look-up. dealing. is low. mathematical. NP-. implementation. (2+2ln2)R. CSF. can 4.. is not. the. complexity on. plexity known. actually. easily.. only. function. shown. can. a. a sum-of-subsets. that. supertrees. arrangement. sorted is. On the. the. requires. selection. WTGM but. shows. in WTGM. is. the. extraction need. and. equals. store. n is much. testing. In the. subband. simulation,. will. 512•~512. computa-. mathematics.. itself. However,. study. The. time. problem. problem. to order. are. to 2(N+M+2). is linear. [5].. sum-of-subsets sort. [20]. sum-of-subsets. complete. items. in. and. decomposition.. more. and. 1GRAM lifting. no. our. embedding. the. to. is. mean. size.. Besides,. computation is. From. (c). window. wavelet. array. which. of local. approximately. static. for. variance. L=1.. wavelet. takes O(n2). ity. for. each. 4 level. calculation. can. local. complexity is the. 3•~3. for. after. the. The. is. obtained. bands. and. size.. the see from. watermark. that the. for. WTGM detailed. medium-high. in resisting. common. em-. attack. for. WTGM.. outperforms comparsion.. frequency signal. setting process-.

(12) IEICE. TRANS.. FUNDAMENTALS,. VOL.E91-A,. NO.8. AUGUST. watermarking. for. 2008. 1972. ing, geometric distortions as well as cryptanalysis with better visual perception than WTGM(S1). Due to the difference of watermark embedding location for setting S1 and S2, the results are expected compared with other wavelet based approaches. However, the weakness for the WTGM the tree combination information must be kept secret addresses extra storage space. The extended study working on the design to efficiently reduce this extra. is that which should cost. [2]. Lu. no.10, [3]. [4]. Langelaar. Process.,. S.H.. Wang. T.K.. C.S. marking. [8]. Tsai. [9]. J.L.. no.4, [10]. scheme,•h. Trans.. for. Process.,. Im-. copyright. vol.13,. no.2,. 2004, Sze,. differscheme,•h. Feb. tree. 2005.. quantization. 2004.. Liao, •gCocktail. IEEE. tree. Trans.. July. group. IEEE. water-. Multimed.,. vol.2,. modulation. International. Processing,. IEEE. (WTGM). Conference. vol.II, effects. of images,•h. on. pp. 173-176,. of a visual. Trans.. Inf.. 2007.. fidelity. Theory,. cri-. vol.20,. 1974. Tang, •gA. IEEE. H.Y.. Sakrison, •gThe. S.X.. pp. 768-775,. pp. 219-230,. and. protection,•h. Signal. encoding. and. robust. 2000.. and D.J.. of optimal. a modified. no.2,. Shen, •gWavelet. pp. 525-536,. ing. Image. of wavelet. watermarking,•h. and. Huang. contrast-sensitive. Multimedia,. vol.13,. visible. no.2,. watermark-. pp. 60-66,. April-June. 2006. [11]. L. Yong,. L.Z.. based. wavelets. on. vol.27, [12]. A.P.. Cheng,. no.11,. L.R.. perceptual. Z.H.. Xu, •gTranslucent. error-correct. Iyer,. masks. Digital. and. and. pp. 1533-1539.. Beegan,. Signal. for. code,•h. Nov. and. Bell, •gDesign. image. Processing. watermark. J. of Computers,. 2004.. A.E.. wavelet. digital. Chinese. and. compression,•h. Workshop,. pp. 88-93,. evaluation. of. Proc.. 10th. IEEE. IEEE. CS. Press,. 2002. [13]. M.J.. Tsai. and. C.W.. watermarking. by. models,•h 1437, [14]. Trans.. S. Voloshynovskiy, approach Proc.. Dresden, B.. 3rd. based. multipurpose. watermarks. with. Fundamentals,. Schneier,. A.. Herrigel,. to. content. Int.. color. human. image. vision. vol.E91-A,. Sept.. Applied. Code. in C,. PGP,. [17]. P.L'Ecuyer, •gUniform. N.. no.6,. Baumgaertner,. adaptive. Workshop. Germany,. [16]. system pp. 1426-. and. digital. Information. T. Pun, •gA. image Hiding,. watermarkpp. 211-236,. 1999.. Cryptography:. Second. ed.,. Protocols,. John. Wiley,. Algorithms,. New. York,. and. 1996.. http://www.pgp.com/downloads/freeware/index.html. erations [18]. dual. 2008.. stochastic. [15]. Lin, •gWavelet. using. IEICE June. Source. 1) The group information for trees needs to be kept for watermark extraction, which needs more storage space. 2) The tolerance for geometric attacks is sill insufficient, feature-based or other RST (Rotation, Scaling and Translation) invariant mechanisms can be taken into account for better synchronization.. C.H. the. B.B.. IWDC. Dec.. Speech. on. 2001.. and. vol.53,. C.J.. image. image. Mannos. terion. ing,•h. On the other hand, there are still some issues needed to be further studied as following:. digital. Acoustics,. Jan.. energy. IEEE. quantization. Trans.. (DEW). Huang,. and. digital. video,•h. J. Mitra, •gCryptanalysis. Process.,. pp. 209-224,. M.J. for. and. scheme,•h. for. vol.10,. differential. and. tree. IEEE. S. Maitra, •gCryptanalysis. S.K.. image. Process.,. 2004.. Signal. and. Lu,. no.4,. pp. 148-158,. watermarking. Trans. Das. images. Lin, •gWavelet. S. Maitra,. watermarking [7]. Y.P.. Feb.. energy. T.K.. no.1,. watermarking,•h. Das,. IEEE. 1) The proposed algorithm can tolerate more common signal processing and geometric attacks. 2) The length of the image key is large, which renders a better confusion/diffusion for security. 3) The human visual characteristics are considered in the wavelet tree based watermarking systems to provide a better visual quality. 4) The watermark can be public for users, and if any malicious user tries to destroy the watermark and sell those attacked copies, the user could be identified.. and. Image. Lagendijk, •gOptimal. encoded. vol.10,. Trans.. 2001.. R.L.. of DCT. age. IEEE. Oct.. and. pp. 154-165,. [6]. Liao, •gMultipurpose protection,•h. pp. 1579-1592,. G.C.. ential. provides a better visual quality of the watermarked image. Compared with the WTQ scheme, the advantages of the proposed algorithm are as follows.. H.Y.M. and. protection. Conclusion. An efficient differential energy watermarking algorithm based on wavelet tree group modulation has been presented. In the proposed algorithm, the watermark is embedded in the relatively high-frequency components using the group strategy for each super tree such that energies of sub-super tree A and that of sub-super tree B are close. The employment of wavelet tree structure, sum-of-subsets and positive/negative modulation effectively improve the robustness of the watermark. The consideration to the CSF and NVF of the HVS. and. watermarking. [5]. 6.. C.S.. authentication. random. Research,. vol.53,. JPEG. 2000. compression,. JPEG. 2000). published. number. pp. 77-120, the from. generation,•h. Annals. of Op-. 1994.. International ISO/IEC,. standard [Online]:. (IS. 15444-1:. http://www.ece.. uvic.ca/mdadams/hasper/•` [19]. StirMark,. http://www.petitcolas.net/fabien/software/StirMarkBen. chmark_4_0_129.zip [20]. Acknowledgments. I. Daubechies. 1 and. lifting. Journal. no.3,. 1. This work was supported by the National Science Council in Taiwan, Republic of China, under NSC95-2416H009-027 and NSC96-2416-H009-015. 2. Partial technical background has been presented in the conference ICASSP 2007 [8].. References. [1]. I.J.. Cox,. J. Kilian,. spectrum cess.,. watermarking vol.6,. no.12,. F.T.. Leighton, for. and. multimedia,•h. pp. 1673-1687,. Dec.. T. Shamoon, •gSecure IEEE 1997.. Trans.. spread Image. Pro-. [21]. R.. steps,•h. pp. 247-269, Sedgewick,. Structures, dison. Wesley,. W. Sweldens, •gFactoring of Fourier. May. 2001.. wavelet and. transforms. into. Applications,. vol.4,. 1998.. Algorithms Sorting,. Analysis. Searching,. in. C, and. Parts Graph. 1-5:. Fundamentals,. Algorithms,. 3rd. Data ed.,. Ad-.

(13) TSAI and SHEN: DIFFERENTIAL. ENERGY BASED WATERMARKING. ALGORITHM 1973. Min-Jen Tsai received the B.S. degree in Electrical Engineering from National Taiwan University in 1987, the M.S. degree in Industrial Engineering and Operations Research from University of California at Berkeley in 1991, the Engineer and Ph.D. degrees in Electrical Engineering from University of California at Los Angeles in 1993 and 1996, respectively. From 1996 to 1997, he was a senior researcher at America Online Inc. In 1997, he joined the Institute of Information Management at the National Chiao Tung. University. search. interests. sic, digital. in Taiwan include. watermarking. puting for electronic Eta Kappa Nu.. and is currently. multimedia. system. and authentication,. commerce.. an associate web. services,. Dr. Tsai is a member. Chang-Hsing. professor.. and applications,. Shen. His re-. digital. foren-. enterprise. com-. of IEEE,. ACM,. and. has received B.S. de-. gree in information management from National Central University in 2000, the M.S. degrees in Institute of Information Management at the National Chiao Tung University in the year 2006. From year 2002 to 2003, he was in the development team of anti-virus service at Trend Micro. He later joined Inventec Besta in 2006 and focuses on digital entertainment and learning of mobile device..

(14)