III. Proposed Algorithm for Copyright Protection and Image Authentication
3.4 Image Authentication (Semi -Fragile watermark) Algorithm
It is difficult to develop a visible watermarking algorithm that can avoid the watermark to be destroyed maliciously by expensive human labors using any software, especially while the texture content of the host image is uncomplicated. In order to detect such kind of tampering and verify the integrity of the visible watermarked images, we modified the image authentication (semi-fragile watermark) algorithm from [31] into the proposed visible watermarked image as a dual watermarking scheme for our complete architecture.
Semi-Fragile Watermark Generation and Embedding Algorithm:
The flow chart of semi-fragile watermark embedding is shown in fig. 8. The semi-fragile watermark embedding procedure are as following:
(1) Select parameters: K1 and K2 are the private keys of the scheme. q1 and q2 are the quantization parameters.
(2) Select the Y (Luminance) component from 3.3 and compute the 2-level 2-D wavelet coefficients of it by using Bi18/10 filter,
r c ×
is the size of LL2.(3) We refer to [35]’s chaotic system called toral automorphisms as chaotic map to get high security watermark. For general applying our algorithms, we also can use scrambling techniques like shuffle to get high security watermark and to solve the issue that the toral automorphisms only suits to square images by transforming two-dimensional matrix to one-dimensional matrix. Map Qnum= ⎢⎣LL q2/ 1⎥⎦ and K1 as controlling parameter. Using equation (9) (10), we obtain the binary watermark W i j( , )∈{ }0,1 ,1≤ ≤i r 1≤ ≤j c.
'
(4) We use K2 as random seed to create two-dimensional pseudo-random array
{ }
( , ) 1, 2, 3 ,1 1
location i j ∈ ≤ ≤i r ≤ ≤j c to determine the watermark embedding location corresponding to
{
LH HL HH . 2, 2, 2}
(5) The binary watermark is embedded into the visible watermarked image by using simple odd-even quantization. We define odd-even quantization function in formula (11) (12) (13) (14) (15). The formula performs quantization on ( , )X i j into odd-even region according the binary watermark W. q2 is the quantization parameter.
2
{ }
2( , ) ( ( , ), , ) 0,1
y i j = f x i j W q x∈R W ∈ q ∈Z+ (11)
Note: (i, j) indicates the spatial location. X is the decomposed wavelet coefficients of the visible watermarked image.
⎣ ⎦ ⎣ ⎦ (6) Perform quantization on wavelet coefficients as follows pseudo code:
2 2
(7) Inverse transform the DWT coefficients of the Y component. The Y component with visible and semi-fragile watermark is converted in the color space domain from YCrCb to RGB.
Fig. 8 The flow chart of the proposed semi-fragile watermark approach
Semi-Fragile watermark Authentication and Tamper Detection algorithm:
The Fig. 9 shows the flow chart of watermark detection scheme, which is similar to the part of semi-fragile watermark embedding. The tamper detection procedure as follows:
(1) Select parameters: K1 and K2 are the private keys of the scheme. q1 and q2 are the quantization parameters. The value of K1, K2, q1 and q2 are the same in embedding and extraction processes.
(2) The obtained visible watermarked image is converted in the color space domain from RGB to YCrCb.
(3) Select the Y (Luminance) component and compute the 2-level 2-D wavelet coefficients of it,
r c ×
is the size of LL2.(4) Use K1 and K2 to create two-dimensional pseudo-random arrays;
{ }
'( , ) 0,1 ,1 1
W i j ∈ ≤ ≤i r ≤ ≤j c and location i j( , )∈
{
1, 2, 3 ,1}
≤ ≤i r 1≤ ≤j c.(5) According to the ( , ) , we find the sub-band and the quantized coefficient, defined asu i j( , ). The extract watermark may be obtained by the following formula (16):
location i j
''
2 m od
( , ) ( ( ( , ) / ) ) 2
W i j = ⎢⎣ u i j q ⎥⎦ (16)
(6) Having obtained two watermarks W'andW'', we define the tamper detection matrix as formula (17), If ' '', then T=0. It means the visible watermarked image was not tampered. Otherwise, the ‘1’ element in the tamper detection matrix indicates the pixels that were tampered.
W =W
' '
T = W −W ' (17)
(7) Since the algorithm is designed to be semi-fragile watermarking scheme which would want to be robust to mild modifications in all cases, it is inevitable that we can’t detect all malicious attack in pixel-wise. However, for practical cases such as removal visible
watermark using neighbor pixels and image cropping which crops objects from a source and pastes them onto a target, the malicious attacks always be applied in a certain region in the watermarked image. That is to say, we assume tamper pixels are always continues.
Therefore, for a certain tamper detection matrix element ( , )T i j , if the number of tampered neighboring element for ( , )T i j is greater than a given threshold, we regard
( , )
T i j as a tampered one. The summary of such post-pro operation of tamper detection matrix is shown as following formula (18):
cessing
(8) According to the DWT decomposition of the watermarked image, the size of tamper detection matrix is
r c ×
, which is about1/16 of the watermarked image. Thus one element in the matrix indicates a corresponding 4 4(18)
× block in the watermarking image.
Finally, we rescale the tamper detection matrix to have the same size of the watermarked age and obtain the tamper detection image.
im
Fig. 9 The flow chart of authentication and tamper detection algorithm approach
IV. Exper lts and
ent their method by assuming the T1=1 and T2=350 to strong the energy of the waterm
ance analysis can be categorized as following:
imental Resu Discussion
The proposed visible and semi-fragile watermarking algorithm has been implemented and intensively tested by using the commonly available color images from USC image database [36]. Because the evaluation standards for visible watermarking system are absent, we would compare our algorithm with previously proposed ones. To make the fair comparison with other visible watermarking considering HVS, the simulation of [9] is highly addressed here instead of the approaches from [4, 10-17]. Since the CSF based visible watermark technique from [9] has shown better performance than the methods from [16] and AiS Watermark Pictures Protector [37], we compared the results by [9] with the proposed approach and the performance of 512×512 colors images. In the Huang and Tang’s method [9], they didn’t describe the value of two thresholds used to classify the blocks of each subband, so we will implem
ark.
Two grayscale watermarks of logo image are embedded for illustration in Fig. 10 (a) NCTU LOGO (school logo) and Fig. 10 (b) IIM logo (department logo). The performance of 512x512 experimental images is tabulated in Fig. 11~18 for comparison purpose. Fig. 11~18 (a) show the original host images, these test images are named “Lena”, “Baboon”, “Lake”,
“Peppers”. Fig. 11~18 (b) the results of the method in Huang and Tang’s watermarking algorithm from [9] are compared with the proposed approach and the results are in Fig. 11~18 (c). The perform
Fig. 10 Two watermark images：(a) NCTU logo (b) IIM logo
(a) (b) (c)
Fig. 11 (a) original Lena image (b) watermarked Lena image by the method in Huang and Tang (c) watermarked Lena image by the proposed algorithm
(a) (b) (c)
Fig. 12 (a) original Lena image (b) watermarked Lena image by the method in Huang and Tang (c) watermarked Lena image by the proposed algorithm
(a) (b) (c)
Fig. 13 (a) original Baboon image (b) watermarked Baboon image by the method in Huang and Tang (c) watermarked Baboon image by the proposed algorithm
(a) (b) (c)
Fig. 14 (a) original Baboon image (b) watermarked Baboon image by the method in Huang and Tang (c) watermarked Baboon image by the proposed algorithm
(a) (b) (c)
Fig. 15 (a) original Lake image (b) watermarked Lake image by the method in Huang and Tang (c) watermarked Lake image by the proposed algorithm
(a) (b) (c)
Fig. 16 (a) original Lake image (b) watermarked Lake image by the method in Huang and Tang (c) watermarked Lake image by the proposed algorithm
(a) (b) (c)
Fig. 17 (a) original Peppers image (b) watermarked Peppers image by the method in Huang and Tang (c) watermarked Peppers image by the proposed algorithm
(a) (b) (c)
Fig. 18 (a) original Peppers image (b) watermarked Peppers image by the method in Huang and Tang (c) watermarked Peppers image by the proposed algorithm
4.1 Visual Effect
From Fig. 11 (b) (c) 、Fig. 15 (b) (c) and Fig. 17 (b) (c), the proposed method has the closest luminance and chrominance maintenance compared with the original ones which are shown clearly from the photos even the difference is sometimes identified subjectively. The watermarked images by using [9] have more bright effect in the unmarked areas. On the other hand, translucence effect is one of requirements for an effective visible watermarking algorithm. The results from our proposed method have better translucence effect than Huang and Tang’s method to make photos look more natural, because the watermarked images by using [9] affect the details of the host (original) image more, especially in Fig. 15 (b) (c) and Fig. 16 (b) (c).
To further compare the details from the watermarked images, Fig. 19 demonstrates some of close-ups for comparison. Fig 19 (a) are the close-ups from original image. Fig 19 (b) are the close-ups from the watermarked images by using [9]’s method. Fig 19 (c) are the close-ups from the watermarked images by using our proposed scheme. It is very clear that the watermark’s edges and thin lines are blurred in Huang and Tang’s method contrast to our results. However, the watermark patterns in our proposed method still have sharp edge and the logo watermark is evidently embedded. For the text pattern, the text of character A in our results is with sharper edge than the same character in results from Huang and Tang’s method.
In addition, the outlines in our results are clearer than those from Huang and Tang’s method.
(a) (b) (c)
(a) (b) (c)
(a) (b) (c)
(a) (b) (c)
(a) (b) (c)
Fig. 19 The visual comparison of close-ups for images to figure 11 through 18 (a) close-ups of the original images (b) close-ups of the watermarked images by the method in Huang and
Tang (c) close-ups of the watermarked images by the proposed algorithm
4.2 PSNR (Peak Signal-Noise Ratios)
To make a fair comparison with the method from [9], it is better to embed the same watermark for the same cover image. However, the watermark used in [9] is not available. We then embed two logo watermarks from Fig.10 to make the best effort for performance comparison. The tabulated results from TABLE 2 disclose that our watermarking scheme are with better statistical results and achieve higher PSNR values than the method in [9] where the PSNRs are generally below 30dB for different images. The low PSNRs have positive correlation with the degradation in image quality. This denotes the fidelity of images from our method is better than the Huang and Tang’s method. In addition, the PSNR values of dual watermarked images are only 0.2~0.4 less than those of visible watermark only images. This means that our proposed multipurpose design could achieve as good as high image quality of visible watermarking but also with extra function of invisible watermarks.
Table 2 PSNR summary of watermarked color images
Image Watermark
4.3 JPEG 2000 Compression
We use StirMark software to test the robustness of the visible watermark and analyze the attacking results. We can clearly find the attacks from jpeg compression and median filter have ability to affect the structure of the visible watermark. In inverse, others attacks like rotation、noise are not able to influence the visible watermark. From above observations, we will list the results form jpeg compression and median filter as follows.
The robustness of the proposed dual watermark technique should be tested for comparison. For JPEG 2000 compression, software from [38] is adopted as the compression tool. The PSNR values before and after the jpeg 2000 compression are tabulated in TABLE 3.
The compression ratio is 100:3 between the uncompressed image and compressed image.
There are two columns of PSNR values for both methods labeled “after”. The pure “after”
column means those PSNR values are compared between the compressed watermarked image and the original image. The after (wn) column means those PSNR values are compared between the compressed watermarked image and the watermarked image. From TABLE 3, we can find that the PSNR values are almost the same for both methods while the compressed watermarked images are compared with the watermarked images (after (wn) column).
However, the PSNR values are higher while the compressed watermarked images are compared with the original images by the proposed approach than by the method of [9] (after column). Therefore, this statistic indicates that the image quality of watermarked image before and after compressed is higher by the proposed approach than the method of [9]. To further investigate the effect of compression, the visual difference can be illustrated by the close-up comparison. Fig. 20(a) show the close-ups of original images. From compression ratio of 100:3, Fig. 20(b) are the close-ups of watermarked images by Huang and Tang’s method. Fig. 20(c) are the close-ups of watermarked images by our proposed method. By comparing Fig. 30, the compressed images maintain the details of the logo pattern but the
characters E, S, A of watermarked images by our proposed method are more apparent than one of watermarked images by Huang and Tang’s method. In addition, the stripes of logo pattern of watermarked baboon image are almost disappearing in Huang and Tang’s method but still existing in our proposed method. This observation is consistent with the claim of our discussion in section II that the embedding factors in [9] emphasize more weights in the low frequency domain instead of the medium-to-high frequency domain while the high frequency components of the watermarked image easily suffer common image signal processing attacks like compression. Therefore, we can indicate that our proposed method is more robust than Huang and Tang’s method by jpeg 2000 compression attack from above observation where the visibility of watermark is surely higher by the proposed approach.
Table 3 PSNR summary of watermarked color images before and after JPEG 2000 Compression
(a) (b) (c)
(a) (b) (c)
(a) (b) (c)
(a) (b) (c)
Fig. 20 The visual quality comparison of close-ups of watermarked image after jpeg 2000 compression ratio of 100:3 (a) original image (b) watermarked images by the Huang and
Tang’s method (c) watermarked image by the proposed algorithm
4.4 Median Filter
The robustness of Median filtering attack is also tested here and StirMark [39] software is adopted here for this attack. Since the results of 3×3 and 5×5 median filtering are similar to the illustration as shown in Fig. 11 ~ Fig. 18, a stronger attack as 7×7 median filtering is applied here for the comparison. The PSNR values before and after the median filtering are tabulated in TABLE 4. There are two columns of PSNR values for both methods labeled “after” and their meaning is the same as mentioned in the session of jpeg 2000 compression. From TABLE 4, we can find that the PSNR values are almost the same for both methods while the filtered watermarked images are compared with the watermarked images (after (wn) column).
However, the PSNR values are higher while the filtered watermarked images are compared with the original images by the proposed approach than by the method of [9] (after column).
Therefore, this statistic indicates that the image quality of watermarked image before and after filtered is higher by the proposed approach than the method of [9]. To further investigate the effect of median filtering, the visual difference can be illustrated by the close-up comparison.
Fig. 21(a) are close-ups of original images. Fig. 21(b) are close-ups of 7x7 median filtering of watermarked image by the Huang and Tang’s method. Fig. 21(c) are close-ups of 7x7 median filtering of watermarked image by the proposed method. By comparing Fig. 21(b)-(c), the median filtered images became blurry but Fig. 21(c) has sharper contour than Fig. 21(b). It is apparent that the logo pattern (i.e. the characters of E, S, A, or the characters of 1896) is still evidently existed in Fig. 21(c) but is blurred and hard to be recognized in Fig. 21(b).
Therefore, the proposed technique outperforms [9] by the median filtering attack from above observation where the visibility of watermark is surely higher by the proposed approach.
Other attacks from [38] are also preformed and the experimental results are consistent with the above findings which indicate our visible watermarking scheme has better visual effect and high PSNR values than other schemes like [9]. In summary, an intensive comparison for
proposed technique has been illustrated above. Different attack and visual quality comparison is also illustrated. Therefore, we can conclude that the proposed method is more robust with better image quality than the algorithm in [9].
Table 4 PSNR summary of watermarked color images before and after Median Filter
Image Watermark
PSNR value (dB)
Method of [9] Proposed method Before After After
(wn) Before After After (wn)
Lena NCTU 27.0 21.2 24.7 31.2 23.1 24.4
Lena IIM 26.8 21.3 24.7 32.3 23.2 24.7
Baboon NCTU 27.1 17.7 19.4 29.9 18.5 19.9
Baboon IIM 27.2 17.8 19.4 30.7 18.5 19.9
lake NCTU 26.2 19.3 21.8 30.5 20.7 21.9
lake IIM 26.1 19.4 21.9 31.3 20.8 22.1
Peppers NCTU 26.9 18.4 20.8 31.1 19.8 20.6
Peppers IIM 26.9 18.6 20.8 32.1 19.9 20.7
(a) (b) (c)
(a) (b) (c)
(a) (b) (c)
(a) (b) (c)
Fig. 21 The visual quality comparison of close-ups of 7x7 median filtering of watermarked image (a) original image (b) watermarked images by the Huang and Tang’s method (c)
watermarked image by the proposed algorithm
4.5 ICA (Independent component analysis) Image recovery Attack
Since the visible watermark is embedded with the images, it is not unusual that attacks would try any means to remove the watermark so they can use the images freely without any copyright concern. If the contour of an embedded visible watermark is completely removed or greatly distorted without introducing serious visual quality degradation, it is difficult for the content owner to claim the infringement by the illegal users. Even this situation existing, a good visible watermark scheme becomes the barrier for the attacks since expensive human labors are needed in order to remove the watermark itself.
Regarding the removal technique, the image recovery method [40] can remove visible watermarking patterns consisting of thin lines and a few human interventions of image-inpainting approach of [41] can deal with patterns of thick lines. However, the iterative process of image-inpainting is costly and time-consuming. Pei and Zeng [42] proposed another image recovery algorithm for removing visible watermarks which is simple, fast with less human intervention. The method mainly utilized independent component analysis (ICA), i.e. joint approximate diagonalization of eigenmatrices (JADE), second-order blind identification (SOBI), and FastICA to separate host images from watermarked and reference images. The algorithm included three phases: watermarked area segmentation, reference image generation, and image recovery. In their experiments, five different visible watermarking methods [4, 10-12, 13, 15] and three public domain images are tested. The experimental results showed that their algorithm can successfully removed the visible watermarks, and the algorithm itself is independent of both the adopted ICA approach and the visible watermarking method. Interested readers can refer [42] for detailed information.
In this paper, we propose a novel visible watermarking scheme and are also curious about the performance against the watermark removal attacks. Therefore, we have implemented the method of [42] and tested several public images used in [42] for comparison. Fig. 22 illustrates the recovered images of our implementation for images from [43, 44] and the results are consistent with the finding from [42] where the watermarks were completely removed. By applying the method of [42] to our proposed visible watermarking approach, Fig 33-35 illustrates the results of the watermark removal attack where the logo patterns slightly disappear but still exist and the contours are recognizable in Fig. 23 (b)(d), Fig. 24 (b)(d), Fig. 25 (b)(d) , Fig. 26 (b)(d). Besides, the watermark removal scheme in [42]
can remove the watermark by the method in [4, 10-12, 13, 15] but the proposed approach can resist such attack. We can conclude that the proposed visible scheme certainly outperforms the method in [4, 10-12, 13, 15].
(a) (b)
(c) (d)
Fig. 22 Recovering the public domain image (a) watermarked image (b) recovered image (c) watermarked image (d) recovered image
(a) (b)
(c) (d)
Fig. 23 Recovering the watermarked images from our method (a) watermarked image with NCTU logo (b) recovered image from watermarked image with NCTU logo (c) watermarked image with IIM logo (d) recovered image from watermarked image with IIM
logo
(a) (b)
(c) (d)
Fig. 24 Recovering the watermarked images from our method (a) watermarked image with NCTU logo (b) recovered image from watermarked image with NCTU logo (c) watermarked image with IIM logo (d) recovered image from watermarked image with IIM
logo
(a) (b)
(c) (d)
Fig. 25 Recovering the watermarked images from our method (a) watermarked image with NCTU logo (b) recovered image from watermarked image with NCTU logo (c) watermarked image with IIM logo (d) recovered image from watermarked image with IIM
logo
(a) (b)
(c) (d)
Fig. 26 Recovering the watermarked images from our method (a) watermarked image with NCTU logo (b) recovered image from watermarked image with NCTU logo (c) watermarked image with IIM logo (d) recovered image from watermarked image with IIM
logo
4.6 Tamper Detection
To evaluate the validity of the proposed image authentication algorithm and make up tampered images, we use Adobe Photoshop CS2 for implement of image processing operations. In our experiments, we let parameters q1=30, q2=10, K1=1234, K2=1234, L=1, β=3. Fig. 27~30 (a), (b), and (c) demonstrate the dual watermarked images (visible and semi-fragile watermark embedded), tampered images, and tampering detection images respectively. In Fig. 27 (b), one object (A .com logo) is inserted into the dual watermarked L
the vi dual
watermarked Baboon image. In the top rig rt of the watermark (logo) image, we use neighboring pixels to remove the visible watermark. In Fig. 29 (b), two objects (A .com logo and boat) are inserted into the dual watermarked Lake image. In the top part of the watermark (logo) image, we use neighboring pixels to remove the visible watermark. In Fig.
30 (b), three object (A .com logo and two Peppers) are inserted into the dual watermarked Peppers image. From the detection result of tampered images, the marked points indicate the tampered parts of watermarked image and these parts are located correctly.
ena image. In the shoulder part of the Lena image, we use neighboring pixels to remove sible watermark. In Fig. 28 (b), one object (A .com logo) is inserted into the
ht pa
(a) (b) (c)
Fig. 27 (a) Result (watermarked) image (b) Tampered image (c) Tampering detection
(a)
Fig. 28 (a) Result (waterma
(b)
rked) image (b) Tampered im
(c)
age (c) Tampering detection
age (c) Tampering detection