A New Image Watermarking Algorithm Based on ...

A New Image Watermarking Algorithm Based on Mixed Scales Wavelets M. El Hajjia, H. Douzia, D. Mammasa, R. Harbab, F. Rosb a

IRF-SIC, Faculty of sciences Ibn Zohr, Agadir, Morocco e-mail: [email protected], [email protected], [email protected] b PRISME. Polytech'Orléans, 12 rue de Blois 45067 0rleans CEDEX 2, France e-mail: [email protected], [email protected]

Abstract. Watermarking is a technology for embedding secure information in digital contents (audio, images, video…). In this paper, we propose an effective watermarking algorithm based on discrete wavelet transform (DWT) using mixed scales representation. The watermark is embedded in dominant blocks using quantization index modulation (QIM). These dominant blocks correspond to the textured and contours zones. Experimental results demonstrate that the proposed method is robust to various attacks and improve watermark invisibility. Keywords: Image watermarking, Wavelet, Mixed scales representation, Quantization index modulation.

1. INTRODUCTION Classical encryption technology is an important tool that can be used to protect data transmitted over networks but it might not be able to solve all digital data protection problems. On this basis, the digital watermarking may be used as an alternative means in the protection of different digital multimedia documents (e.g., digital audio, images, video, etc...), and in the diminution of piracy and counterfeiting [1]. In the last few years, digital watermarking has been considered as an efficient means to ensure integrity and authenticity verification. In fact, It was introduced

as a way to improve the security of Identity documents, such as ID cards, passports, driver's licenses and plastic cards[2][3]. It is a matter of fact that these personal documents contain textual information, portraiture of the person and other biometric characteristics such as fingerprints and handwritten signature or a chip in plastic support. Hence, watermarking can be defined as a covert digital security feature that is applied to interlace and secure multiple elements of identity documents and can also be a highly effective method to interlock facial images, physical cards and chips. In image watermarking, a signal (the watermark seen as an intelligent noise) is embedded into the image by exploiting the imperfections of the human visual system [1]. Usually, the mark is hidden in highly informative regions such as texture or contours. Imperceptibility, robustness and security constitute three conflicting watermarking objectives; imperceptibility means that the embedded watermark should not make any perceptual distortion on the host signal. Robustness implies that the embedded watermark should act as a carapace against common degradations (both natural degradations affecting media and attacks). In this respect, an unauthorized party should not be able to destroy the watermark without making the document useless. To ensure robustness, the watermark information has to be incorporated redundantly in the host data so that it can be retrieved

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even in the presence of only a small amount of data. Robustness is also dependent on the strength of the insertion: the stronger the insertion, the better the resistance to different signal attacks is. Security entails that only authorized parties should be able to detect, recover, and possibly modify the watermark. Watermark embedding can be performed in spatial domain[3][4][5] but also in a transform domain such as the full-image DCT (Discrete Cosine Transform)[6][7], DFT (Discrete Fourier Transform) domain[3][8], and sometimes in the fractal domain[9]. Signal embedding is generally done by addition/multiplication or by the use of histograms[10], mostly in the luminance band alone, and sometimes, in colour channels[3][11]. Over the last decade, many watermarking schemes have been developed using Discrete Wavelet Transform (DWT) [11][13][14]. The watermark is usually embedded into the wavelet significant coefficients of the HH, HL or LL sub-bands. In [11], Lin and Wang proposed a watermarking algorithm based on the DWT, the wavelet coefficients are grouped into different block size and blocks are randomly selected from different sub-bands. The watermark was embedded in the local maximum coefficient by quantization. Bohra et al [13] proposed a watermarking technique based on integer wavelet transform. To embed the watermark, LL1 sub-band was used because the perceptual distortion at low frequencies is lessened and, hence, a strong watermark can be embedded. Yuan et al [14] proposed an Integer Wavelet based on multiple logo-watermarking schemes. The watermark was embedded by modifying the coefficients of the HH and LL sub-bands. Each watermarking application has its own needs that determine the required attributes of the watermarking system, and drive the choice of techniques used for embedding and detecting the watermark. The robustness to different image processing attacks is the key challenge and the algorithms in literature addressed only a subset of attacks. The tradeoff between imperceptibility and robustness is managed by the insertion scheme itself and is also controlled by the mark effect or degradation on the image. This control is commonly ensured by the use of a psycho-visual mask aiming at

modelling the behaviour of the Human Visual System (HVS)[15][16]. The most challenging issues for these algorithms are adapting insertion to HVS. In most cases, the visual mask is created in the spatial domain. We propose here a new approach in DWT watermarking based on mixed scales representation, which offers an efficient modelling of the HVS. It performs an implicit visual masking as only wavelet dominant coefficients are selected for watermark insertion. These coefficients correspond to regions of texture and to edges of image. Douzi et al. [17] use DWT mixed scales to refine edge detection and image characterization by selecting regions with a high density of significant wavelet coefficients. To the best of our knowledge, this way of generating wavelets has never been used in the watermarking area. In DWT mixed scales, the information is only presented in a small number of wavelet coefficients called the dominant coefficients. They are located in highly informative regions of the image such as contours and textures. So, ‘DWT mixed scales’ seems to be a natural candidate for watermarking. The wavelet decomposition is achieved using either convolution or lifting scheme. In our work, we have chosen lifting process given that often requires less computation and requires less memory to calculate wavelet transforms [21]. To embed the watermark bits, we check basically tow methods: additive and substitutive watermark. In the first case, many proposed watermarking techniques add a spread spectrum signal to the host image[19], because the unmodified host image cannot be subtracted from the received data in blind schemes, this leads to interference when correlating the watermark with the received signal, so the detection of the watermark is threatened to be loosed or confused with the signal of the image itself. However, Chen et al. [18] have proposed the QIM (Quantization Index Modulation), the substitutive method proposed consists on coding the message by modifying the original data itself , where the elements of the message act as an index that select the quantizer used to represent them. So, the analyses of the received data allow reconstruction of the message without leading to any kind of interferences. In fact, the originality of our approach stems from the mixed scales approach and its hybridization with QIM.

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This paper is organized as follows. In section 2 and 3, we present the proposed approach. In section 4, we expose experimental results. The conclusion and prospective works are described in the last section.

2. DWT MIXED SCALES In this section, we first give a brief review of the wavelet representation of an image. We further outline DWT which is based on lifting schemes. Also, we outline the mixed scales representation.

A. Discrete Wavelet Transform (DWT) Wavelets have been found to be extremely useful in digital watermarking. They have a growing impact in signal and image processing, mainly due to multistage and time-frequency localization of the image. In addition, their good performance in decorrelating information is an interesting point. The basic idea of the DWT for an image is described as s illustrated in Figure 1. An image is first decomposed into four parts low-low (LL), low-high (LH), high-low (HL) and high-high (HH) sub-bands [15] by critically subsampling horizontal and vertical channels using subband filters. The subbands labeled HL0, LH0, and HH0 represent the finest scale wavelet coefficients. To obtain the next coarser scaled wavelet coefficients, the subband LL0 is further decomposed and critically subsampled. This process is repeated several times, which is determined by the application at hand. Each level has various bands information such as low–low, low–high, high–low, and high–high frequency bands. The original image can be reconstructed using the inverse DWT (IDWT).

B. Mixed scales In the proposed watermarking algorithm, we opt for mixed scales representation of an integer wavelets transform qualified by Faber-Schauder wavelet transform (FSDWT)[17]. It based on the Lifting Scheme[21] without any boundary treatment. The FSDWT is well adapted to detecting textured and contours regions. So, it offers a promising

environment for robust watermarking with efficient modelling of the HVS. The FSDWT redistributes the information contained in the original image. In each sub-band, the most of the information of the image is carried in the dominant coefficients where we can embed the watermark with a high strength without making any perceptual distortion on the original image. In order to facilitate the selection of theses dominant coefficients in all sub-bands, we use mixedscales representation which puts each coefficient at the point where its related basis function reaches its maximum as shown in Figure 2. So, a coherent image can be visually obtained with edges and textured regions formed by dominant coefficients. These regions are represented by a high density of dominant coefficients. They present more stability for any transformation keeping visual characteristics of the image. Also, the coefficients associated with a watermark bit are distributed spatially throughout the image and distributed across a variety of low-, mid-, and high-frequency coefficients. This provides robustness to clipping and other spatially localized processing and frequency filtering.

3. WATERMARK EMBEDDING In this section, we present the watermark embedding based on FSDWT in mixed scales. The embedding step is performed by blocks of dominants FSDWT coefficients using quantification index modulation (QIM).

A. Dominant blocks We use statistical features of the FSDWT coefficients to select these dominant blocks. In [17], Douzi et al. use two thresholds Sc and Sd to separate the dominant blocks from no-dominant ones. The first threshold Sc is used to select the significant coefficients and the second Sd is used to select the most dense blocks. However, these thresholds must be adjusted manually. To reduce the number of thresholds and to fix them automatically, we use the standard deviation of FSDWT coefficients.

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Figure 1. Multiresolution (separated levels) representation As shown in Figure 3, most of the DFSWT coefficients have amplitude near to zero. The dominant coefficients are far from zero and can be selected using this property. Indeed, this property can be explained by the fact that the number of coefficients in DWT increases exponentially when we move up sub-bands, so the concentration of significant value coefficients becomes important in a region which uses a maximum number of scales: i.e. very shape variation regions. This implies, first, that there are only few significant value coefficients describing high luminous transition regions in the image and, second, most of these significant value coefficients are concentrated around image contours and textures which correspond in general to facial features in an ID image. Most of other close-to-zero coefficients correspond to low luminous variation regions which correspond, in general, to the background. The dominant coefficient blocks play a major role in modelling relevant characteristics of the image and offer a perfect balance between the image quality and its robustness due to the fact that, as shown in Figure 4, they are located mainly around the image contour and characterize textured zones. The following rule is used: A block is dominant if σ ′ ≥ σ , σ and σ ′ are respectively the standard deviation of mixed scales DWT coefficients and the local deviation for a given 8×8 block.

Figure 2. Mixed scale representation of the Lena image

Figure 3. Histogram of FSDWT coefficient values.

B. Quantization Index Modulation (QIM) embedded One of the most popular families of watermarking methods is spread-spectrum (SS). These methods modify linearly the host signal to embed some information. However, the watermarked signal acts as an additive interference to estimate correctly the watermark message m. Consequently, these methods can usually embed only a small amount of information and are useful primarily either for non-blind schemes or when the host signal interference is much smaller than the channel interference. Quantization QIM schemes perform non-linear modifications and detect the embedded message by quantizing the received samples to map them to the nearest reconstruction point.

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(a) The dominant blocks

(b) Selected zones in original image

Figure 4. The dominants blocks of the image”Lena” The basic idea of QIM methods is the quantization of signal to two sets of discontinuous points according to the watermark bits. For example, consider the case of the watermark = m 1= or m 0 , the signal x is divided into two sets of disjoint points A and B using a quantization function Q as shown in Figure 5. The set A corresponds to the watermark bit 0 and set B to the watermark bit 1. According to the watermark bit to be embedded, the host signal s is quantized to the nearest point of Q ( x, 0 ) or Q ( x,1) . In detection, if the corrupted signal z lies in the set A, the extracted watermark is 0, otherwise it is 1.

Figure 6, where the original image is first transformed into mixed scales. The second step is to select blocks with high density of wavelet dominant coefficients. The watermark bits can be embedded redundantly when their length is less than the number of the dominant coefficients selected. The sequence of the dominant blocks selected X was then pseudo-randomized and each bit off the message was embedded in 64 random coefficients. The purpose of using randomization of the dominant coefficients is to improve the performances [19]; first, the coefficients associated with a single bit are distributed spatially throughout the image and secondly distributed across a variety of low-, mid-, and high-frequency coefficients. This provides some robustness to clipping and other spatially localized processing and frequency filtering. Concretely, it is produced by quantizing each dominant coefficient xn with the resulting of dithered

Figure 5. Simple example of Quantization Index Modulation, here A and B are the sets of circles and crosses, respectively. Dither-modulation QIM (DM-QIM) is the most used method [16]. On the one hand, pseudorandom dither signal used by DM-QIM can reduce quantization artifacts to produce a perceptually superior quantized signal. Also, dither ensures that the quantization noise is independent of the host signal. On the other hand, the pseudorandom dither signal can be used as a key k to improve the security of the system which is only known to the embedder and detector. Before embedding, the image is first pre-processed through histogram egalization in order to increase the dynamic. The embedding algorithm is illustrated in

quantizer to form the watermarked block

yn

according to:

= yn Q( xn + d (n, mn ), ∆) − d (n, mn ) Where

n = 1..size ( m )

and

d ( n, 0 )

(1)

is

a

pseudorandom signal with uniform distribution over

 −∆ ; ∆  using a secret key k.  2 2 And Q ( x, ∆ ) is a uniform quantization function with step ∆ defined by:

x = Q ( x, ∆ ) round   ∆ ∆

(2)

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The quantization step size is empirically adjusted to control the watermark strength and the documentto-watermark ratio and must be known to the detector.

are selected in the same way that the embedding phase does. The secret key k is used to generate the dither sequences d (n, 0) . During detection, we calculate

The third step consists of embedding the watermark with the dominants blocks to create the signal y according to (1). Finally, the signal y is transformed by IFSDWT to get the watermarked image I*.

two signals by embedding “0” and “1” into the received signal separately, in the same manner as the equation (1):

S z (n,= 0 ) Q( zn + d ( n, 0 ) , ∆) − d (n, 0 ) (3) S z (n= ,1) Q( zn + d ( n, 0 ) , ∆) − d ( n,1)

C. The watermark detection Our watermarked method is blind since the detection processes do not require the use of the original image. The watermarked receives a distorted ˆ watermarked image z and extract a message m knowing the key k. First, the watermarked image is transformed into mixed scales of FSDWT. Then the dominant blocks

The detected message is retrieved based on the determining which of these two signals has the minimum Euclidean distance to the received signal z according to following equation:

mˆ = arg min dist ( z , S z ( n, l ) )

(4)

.

Figure 6. The proposed embedding algorithm.

4. EXPERIMENTAL RESULTS In order to evaluate the performance of our method for watermark detection and robustness to attacks, we used a database of 200 identity images each with a size of 512 × 512 and a binary message with a of length of 32 ×16 bits is embedded into each image. The Peak Signal to Noise Ratio (PSNR) is used as distortion measurement between the original and a watermarked image. It is define as:

  PNSR = 10 log10    

  255  N 2  1 N ∑ ( wn )  n =1  2

(5)

Where N is the total number of the pixels, w= xn − yn , x represents the original image, n and y is the watermarked image. The Bit Error Rates (BER) between the extracted watermark Wˆ and the original W are used to measure

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the robustness of our scheme; the bit error rates (BER) is the percentage of bits that have errors relative to the total number of bits detected. Figure 7 shows the BER as a function of step size ∆, when the quantization step is larger, we can detect the watermark without errors. The detection efficiency is significantly decreased for small step size due to the fact that the noise added by dithering exceeds ∆/4 [16].

watermarked image is about 38.64 dB. We notice that the watermarked image is undistinguishable from the original one. In the difference which is suitably scaled for display, it is evident that watermark data are added to the edges and the textured regions where they are not perceptible as shown in Figure 8(e). The robustness of our method was tested against different types of malevolent attacks, including JPEG compression, additive white Gaussian noise, Sal & Pepper Noise, the median filtering and so on. Figure 9 shows watermarks retrieved after particular attacks. The positions of dominant blocks will be shifted after adding to watermarked image an intensive noise. It is then difficult to extract the watermark. For each attack, we calculate, upon database images, the average of BER between the original watermarks and the detected one. Some test results are listed in table 1. However, the results related to rotation are not reported here, since they depend on a synchronization mechanism. As shown in table1, our technique can be used for robust watermarking. We have observed that a mixed-scale algorithm has less sensitivity to JPEG compression, even if we didn't use a perceptual model.

Figure 7. BER as a function of step size ∆

Figure 8 shows Lena image, the watermarked image and the extracted watermarks, the quality of the

(a) Original image

(b) PNSR= 38.64 dB T=1, ∆=15

(c)

(d) BRE= 0

(e)

Figure 8. (a) The original image. (b) Watermarked image. (c) Watermark (d) Watermark retrieved (e) Scaled difference between original and watermarked images.

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median filter

Scaling factor

AWGN noise

AWGN noise

(3×3)

(+1,5)

(σ=1.5)

(σ=3)

BER=0.16

BER = 0.228

BER =0.04

BER =0.166

JPEG(QF=90)

JPEG (QF=80)

JPEG (QF=50)

BER = 0.0

BER =0.076

BER =0.175

Salt & pepper (0.005)

BER =0.265

Figure 9. Watermark retrieved after some kinds of attacks

Table 1. BER after various attacks for a fixed PNSR= 35 (for database IDs images) Attacks No attacks Median Filtering 3×3 Median Filtering 5×5 Salt & Pepper Noise 0.01 Salt & Pepper Noise 0.02 Additive white Gaussian Noise (σ=1) Additive white Gaussian Noise (σ=2) Additive white Gaussian Noise (σ=2.5) Additive white Gaussian Noise (σ=3) Cropped 10% removed Cropped 25% removed JPEG (DF=50) JPEG (DF=60) JPEG (DF=70) JPEG (DF=80) JPEG (DF=90) JPEG (DF=100)

BER 0 0.203 0.21 0.2082 0.356

can sustain to volumetric attacks more efficiently as compared to Wang and Lin’s method.

Table 2. Comparing the proposed method with Wang and Lin’s (for Lena image) Attacks

0

NC Proposed method (38.64dB)

NC Wang and Lin (PNSR=38.2 dB)

1.00 0.59

1.00 0.51

0.36

0.23

0.69 0.962

0.64 NA

0.73 0.79 0.91 1

0.28 0.28 0.57 1

0.18 0.22 0.226 0.02 0.16 0.203 0.181 0.174 0.159 0.095 0

No attacks Median Filtering 3×3 Median Filtering 4×4 Gaussian noise Cropped 25% removed JPEG (DF=50) JPEG (DF=70) JPEG (DF=80) JPEG (DF=90)

We compare also the proposed method to Wang and Lin’s [25] Methods using the Lena image, their watermarking scheme are based on wavelet-tree quantization. The normal correlation (NC) between the extracted watermark Wˆ and the original W is used to measure the robustness of our scheme. The results are shown in Table 2. It shows that our method

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5. CONCLUSION A novel robust watermarking algorithm in mixed scales DWT domain has been presented. It produces watermarked images that are perceptually indistinguishable from the original ones. It has been experimentally demonstrated that the method gives more interesting results than popular DWT methods based on QIM, especially in JPEG compression and noise. Rotation attacks are not managed by the current version, since they depend on a synchronization mechanism. However, the performance of the algorithm depends on some factors such as the choice of step ∆ . This step ∆ could be fixed to dominant blocks using a perceptual model adapted for FSDWT to improve the performance of the algorithm. A potential work will investigate a synchronization mechanism to improve the robustness against rotation.

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