We propose a new, robust and simple DCT energy based watermarking scheme
for images. We first divide the image into blocks and compute the amount of ...
A ROBUST DCT ENERGY BASED WATERMARKING SCHEME FOR IMAGES Adarsh Golikeri, Panos Nasiopoulos Department of Electrical and Computer Engineering The University of British Columbia, Vancouver, BC, Canada {adarshg, panos}@ece.ubc.ca ABSTRACT We propose a new, robust and simple DCT energy based watermarking scheme for images. We first divide the image into blocks and compute the amount of DCT energy in each block. Blocks containing significant DCT energy, which represent areas of significant detail, are chosen for insertion of a Gaussian pseudorandom watermark. The watermark is inserted in a spread-spectrum fashion in the spectral domain, thereby making it robust against signal processing operations. Our scheme is very easy to implement and is shown to be robust against cropping, scaling, compression distortion and multiple document attacks. We show that our algorithm performs equally well for JPEG compression and Median filtering, when compared existing DCT-based methods, which are computationally more comp lex.
1. INTRODUCTION The need for watermarking of images has gained importance in the past few years, owing to the rapid growth of such digitized media over the internet. Images can now easily be copied and distributed, with little or no control of ownership. Traditional encryption systems exist, which allow only valid key-holders to access data. However, once decrypted, this data is again susceptible to unauthorized reproduction. Therefore, digital watermarking schemes are needed in order to serve as standalone or complementary copyright protection systems. A digital watermark is a secret code carrying identification information about the copyright owner or creator. Generally, a watermark is embedded such that it is invisible. In order to be effective, a watermark should satisfy certain basic criteria: Unobtrusiveness: The watermark should not degrade or affect the image quality in any perceptible manner. Robustness: It should be resistant to attacks, both intentional and unintentional, specifically the following types of attacks:
i) Geometric distortions: Operations such as cropping, scaling, rotation and translation. ii) Collusion attacks: Attempts to destroy the watermark by making use of multiple watermarked images. Also, it should not be possible to generate a new watermarked image by combining several watermarked images. iii) Other operations: This includes digital-to-analog conversion, analog-to-digital-conversion, resampling, requantization and compression distortion (e.g. JPEG). Detection Accuracy: The detection algorithm should have a low false-positive and false-negative rate. Detection of the watermark should be able to prove ownership unambiguously. Two major applications of digital watermarking are copyright protection (proving ownership of data) and data authentication (for use as evidence against crimes). In such cases the data needs to be proved reliable and unmodified. Current techniques for watermarking concentrate mainly on images and can be classified into two groups. The first group is based on spatial domain techniques, which embed the watermark by directly modifying the pixel values in the image. The second group comprises of transform domain methods, which embed the watermark by modulating the transform domain coefficients of the data. The transform methods are more complex, but more robust than the spatial methods. In this paper, we propose a new, simple and robust DCT-based scheme for images. We show through experiments that our scheme satisfies the basic criteria of a watermark mentioned above. Our method is more robust against compression distortion than the existing DCTbased methods [2]. The rest of the paper is organized as follows: Section 2 provides an overview of existing DCTbased watermarking schemes. Section 3 details our proposed scheme. We then discuss experimental results in Section 4 and provide concluding remarks in Section 5. 2. EXISTING DCT-BASED WATERMARKING Cox pioneered the frequency-domain watermarking scheme based on DCT [1]. Cox’s proposed method
computes the N x N DCT coefficients for an N x N image. The watermark of length n is placed into the n highest magnitude coefficients of the transform matrix, excluding the DC component. The motivation for choosing the higher value coefficients is that they represent the low frequency regions of an image, which contain most of the perceptually significant image information. Also, the human visual system attaches more resolution to the lowfrequency spectral components. Further, it has been observed that common signal processing operations and distortions affect the perceptually insignificant regions of an image, which correspond to high-frequency components. So, the watermark has to be inserted in the low-frequency components. A more recent DCT-based method, which uses feature extraction points and the Voronoi diagram can be found in [2]. The authors segment the given image based on the Voronoi diagram and feature extraction points. The complexity of this step is of the order of O(n log n) computations. Then, they embed the watermark into the DCT domain of each image segment. Our scheme has the advantage of computational simplicity, when compared to [2], without compromising on the level of robustness. 3. PROPOSED WATERMARKING SCHEME Our algorithm makes use of perceptually significant DCT coefficients of the image and uses them to carry the watermark information. So, we utilize blocks with high DCT energy. These blocks represent regions in the image with lot of texture and detail. This serves two purposes. First, these regions have a great perceptual capacity, in that modifying their DCT coefficients will not cause visible artifacts. Second, any attack on these regions (malicious or otherwise), will render the image unusable. This serves as a security measure against tampering. Watermark insertion: 1) Read input image to be watermarked (Figure 1(a)). 2) Compute high DCT energy blocks (Figure 1(b), defined as those having at least 30% sum of squared AC energy of the highest energy block ), by first dividing the image into 16x16 blocks and computing DCT for each block. Let n be the number of selected blocks. 3) Compute the watermark sequence W, from an N(0,1) distribution. This type of sequence is particularly robust against collusion attacks. W is an array of length n. We denote the elements of W as wi (where i = 1 to n). 4) For i = 1 to n: i) Select the perceptually significant DCT coefficients of this block – we choose the first 8 AC coefficients of the 16x16 block, in JPEG zig-zag scan order.
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(c) Figure 1. (a) Original image (b) Selected high energy blocks (c) Watermarked image
ii) Alter these coefficients according to : Vi' = Vi ( 1 + a wi) where, Vi' = adjusted coefficient, Vi = original coefficient, a = scaling factor (determines watermark strength). 8) Generate the watermarked image by applying the inverse DCT and merging all blocks (Figure 1(c)). Watermark detection: Given a possibly distorted version of the image, we attempt to extract the watermark and compare it with the original watermarked data. 1) Read the image to be tested. 2) Compute the high energy DCT blocks as in Step (2) of the insertion scheme. 3) For i = 1 to n, extract the watermark estimate W* (which is also an array of length n. 4) Apply the following transformations to W and W* for robustness: x = x - mean(x) (reduce x by its average value) x = sign(x) (convert x into an array of -1, 0 or 1s) 5) Compare W and W* using the correlation coefficient as the similarity measure. 6) Detection is successful if the correlation exceeds the experimentally determined threshold of 0.2. Performance evaluations have shown that this threshold value minimizes the probability of false positives.
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Figure 2. Uniqueness of watermark – detector response to 1000 random watermarks, only one of which matches the watermark present in Figure 1. Dashed line indicates threshold value = 0.2
4. EXPERIMENTAL RESULTS To test our proposed scheme, we used several standard test images such as Cameraman, Baboon, Lena, etc. We display results from the Cameraman image. Figure 1(a) shows the original Cameraman image, while 1(b) shows the high energy DCT block. For all our experiments, we use a scaling factor a of 0.1. Figure 1(c) shows the watermarked Cameraman image. Subjectively, it is indistinguishable from the original image. We conducted the following tests: Uniqueness of watermark: In this test, 1000 random watermarks were generated, out of which only one matched the original watermark. The output of the detector is shown in Figure 2. The response to the correct watermark is 0.9204, which is much stronger than the threshold value of 0.2. This indicates that the proposed algorithm has very low false positive response rates. Geometric manipulations (scaling and cropping): The watermarked image was resized to 50% of its original size (Figure 3(a)) and then rescaled to its original dimensions (Figure 3(b)). The loss of detail in the rescaled image is clearly visible. The detector response to Figure 3(b) was 0.3065, which is well above the threshold. Next, about 40% of the watermarked image was cropped (portion shown in Figure 3(c)) and the remaining pixels were replaced from the unwatermarked image. The detector response to Figure 3(d) was 0.3692. Compression distortion: The Cameraman image was encoded as different JPEG images with parameters of 65%, 45%, 25% and 10% quality (Figure 4(a)-(d)). The detector gave outputs of 0.3571, 0.2576, 0.2440 and 0.2356 respectively. This is in spite of severe JPEG compression
(c) (d) Figure 3. Image scaling – (a) 50% scaled version (b) Rescaled version showing loss of detail (c) cropped portion from watermarked image (d) reconstructed image using the remaining portion of the unwatermarked image
artifacts in the JPEGs with lower quality factors. Collusion attacks: Any good watermarking scheme needs to be sufficiently resistant to collusion (multiple document) attacks, wherein multiple watermarked images maybe be combined to destroy the watermark. In this test, we first generated 5 different watermarked images and then generated a 6th image by averaging them. When the resulting average image (Figure 5(a)) is compared with 1000 random watermarks, the detector gives good correlation values of 0.2861, 0.3978, 0.3423, 0.3247 and 0.2832 for the 5 original watermarks and low values (mean = 0.0039) for the remaining watermarks (Figure 5(b)). This shows that this collusion attack has failed to destroy the uniqueness of the 5 individual watermarks. Comparison of proposed scheme and existing DCT-based method: The Cameraman image was watermarked using our scheme and the method by Suhail and Obaidat in [2], then encoded as JPEGs of different compression ratios. As can be seen in Figure 6, our algorithm performs equally well, without introducing the computational complexity for computing the Voronoi diagram [2]. Also, our scheme gave an output of 0.2345, for a compression ratio as low as 10%. We also compared performance after Median filtering, using different kernel sizes, from 3x3 to 9x9. Both schemes performed similarly, with our scheme giving a slightly better result (0.5122 vs 0.47) for a filter size of 9x9.
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(c) (d) Figure 4. JPEG distortion – with (a) 65% (b) 45% (c) 25% and (d) 10% quality factors
5. CONCLUSION We have presented a new, robust and simple DCT energy based watermarking scheme, based on spread spectrum approach. First, the image is divided into 16x16 blocks and the DCT of each block is computed. Then, high energy DCT blocks, which represent areas of significant detail, are selected and a Gaussian pseudorandom watermark is inserted into perceptually significant DCT coefficients. Our scheme is shown to be resistant against cropping, scaling, compression distortion and multiple document attacks. We have shown that our algorithm performs equally well, compared to computationally more expensive DCT-based methods, while maintaining the same level of robustness.
(b) Figure 5. Collusion attacks (a) Image generated by averaging 5 independently watermarked images (b) Detector output for 1000 random watermarks, 5 of which were used to create (a).
6. REFERENCES [1] I. Cox, J. Kilian, F. T. Leighton and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” ( IEEE Trans. Image Processing, vol.6, pp. 1673–1687, Dec. 1997.) [2] M. A. Suhail and M. S. Obaidat, "Digital Watermarking-based DCT and JPEG model" (IEEE Trans. on Instrumentation and Measurement, Vol 52, No. 5, October 2003) [3] I. Cox, M. Miller and J. Bloom, Digital Watermarking. New York: Morgan Kaufmann, 2001.
[4] F. Hartung and B. Girod, "Watermarking of Uncompressed and Compressed Video", in Signal Processing, pp. 283-301, 1998.
Figure 6. Comparison of proposed scheme and Suhail's method for JPEG Compression [5] P. Wolfgang and E. Delp, "A watermark for digital images", in Proc. IEEE Int. Conf. Image Processing, Sept 1996, pp. 219222. [6] A. Bors and I. Pitas, "Image watermarking using DCT domain constraints", in Proc. ICIP, Sept. 1996, pp. 231-234.
