TECHNICAL REPORT No. TR2000/10/01

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1 TECHNICAL REPORT No. TR2000/10/01 Bit Signature Casting in the DCT domain V. Fotopoulos, P. Kavathas and A.N. Skodras October 2000 Abstract Spread spectrum techniques have been successfully applied in still image watermarking. Pseudorandom noise sequences and transform coefficients of logo images are mostly used in these methods. In this report a new signature casting scheme is proposed. The watermark/signature is a 32-bit sequence and the casting domain is the DCT coefficients of the entire image. The 32 largest coefficients are selected for embedding. The original image is needed for the extraction of the signature. Various tests have been performed and results appear quite promising. TECHNICAL REPORT No. TR2000/10/01 1

2 1. Introduction During the last years, there has been a rapid growth in the use of Internet. People from all around the world exploit Internet s great capabilities by exchanging any possible kind of information and useful data. Along with all the benefits from this situation, it s also a fact that many problems concerning copyright ownership protection issues have arisen. The main reason is that digital media can nowadays be exactly reproduced, easily, and quite cheaply. These problems have led to the development of Digital Watermarking, in order to protect all digital media (image, audio and video) from the so-called electronic piracy. Digital Watermarking is a relatively new field of research with many features similar to Cryptography. Of course, the goal here is to hide information rather than coding it like Cryptography does [1]. The watermark is basically a digital signature embedded into a digital media, for the purpose of ownership verification and/or authentication. In the case of digital still images, the watermark can be a stream of bits that constitute a digital signature, a pseudo random noise sequence or an image pattern. They can be classified as visible or invisible. Visible watermarks are generally easier to destroy and not so valuable because they alter the image significantly. Invisible ones are more important and are used in various applications depending on their persistence factor. The image quality is altered according to the method. In general the most robust the watermark is, the greater the changes are and consequently the image degradation. Speaking of robustness there are applications where this is the main issue, such as intellectual property rights protection while on the other hand less fragile watermarking can be used for transaction proof [2]. In section 2, an overview of the watermarking features is presented. In sections 3 and 4 the embedding and detection schemes of the proposed method are discussed. The experimental results are reported in section 5. Some comments and observations on the results are given in section 6 and the work is concluded in section 7 with some ideas for future research directions. 2. General Watermarking Features The so far proposed watermarking schemes for digital still images obey to some common rules for watermark embedding. For the detection process though, they don t have the same requirements. Depending on the application, a watermarking system may sometimes require the original image in order to detect the watermark or at least answer whether it was found or not when given a copy of it. There are also systems that do not use the original image and have no information about the watermark during the detection procedure. These are called blind detection systems and are of great practical use [3]. A key is usually associated with both procedures. The key is an important part of the procedure, especially in methods that use pseudo random sequences. In these, the key plays the role of the random generator s TECHNICAL REPORT No. TR2000/10/01 2

3 initialisation seed. In some applications there are two types of keys, private ones and public ones. The private key is used in the embedding process and is property of the creator. The public key is used for ownership proof by an authorised owner/client. The diagrams of the Embedding and Detection Processes of a typical watermarking system are shown in Figures 1 and 2. Watermark Key Original Image Embedding Algorithm Watermarked Image Figure 1. Block diagram of the embedding process The basic principles governing the digital watermarking of still images are: 1. The watermark must be perceptually invisible in the majority of applications. 2. The watermark should not be detected by applying statistical processing methods to a number of images with the same watermark. 3. The detection process must be trustworthy and should prove beyond any doubt who the rightful owner of the digital media is. 4. Every watermarking method must have a secret key that controls the embedding/detection algorithm. 5. Each method should be as robust as possible, in order to face any attacks that may be applied to the watermarked image. 6. Each method should give the capability of embedding more than one watermarks into the same image (multiple watermarking). Original image Watermark Key Watermark: YES or NO Test Image Detection Algorithm or... Information about the watermark Figure 2. Block diagram of the detection process TECHNICAL REPORT No. TR2000/10/01 3

4 Many different techniques for digital image watermarking have been proposed throughout the last five years. There are three categories of watermarking techniques as shown in Figure 3. These are: A. Techniques that embed the watermark directly into the spatial domain of an image. A common practice is the modification of the LSB (Least Significant Bit) by a certain factor [4]. Another approach is the addition of each watermark bit to a large number of image bits [5]. Some statistical methods, such as patchwork, have also been proposed [6]. B. Techniques that embed the watermark into the transform coefficients of an image. These methods can be block based or they can be applied to the whole image. The transforms usually involved are the DCT (Discrete Cosine Transform) [7,8], the DFT (Discrete Fourier Transform) [9] and the DWT (Discrete Wavelet Transform) [10,11]. Recently, there have been some attempts using the Hadamard and the Chirp-Z transforms. C. Hybrid techniques that are a combination of the above two. WATERMARKING TECHNIQUES Spatial Domain LSB based Statistical methods Watermark a large number of image bits Frequency Domain DCT DFT DWT Hadamard Chirp-Z Hybrid Techniques Figure 3. Classification of the Watermarking Techniques The embedding place of the watermark is another important issue. Some methods cast the watermark in the whole image, while others do it in specific regions or blocks. The second category usually exploits some special characteristic of the Human Visual System (HVS) [7,8,10]. This has to do with the tolerance of the human eye in certain changes made to the image. For example, if we cast our watermark into a part of the image that is characterised by lots of fine details, the changes imposed by the watermark are not easily observable. TECHNICAL REPORT No. TR2000/10/01 4

5 3. Watermark Embedding According to the method presented in this report, the watermark is inserted into the DCT domain of the host image (Figure 4). The watermark is a bit signature, a stream of 1 and 0 giving a total number of 32 bits. The transform is applied to the image as a whole. More specifically, it is applied to a square block whose side is the nearest power of 2 that can fit into the image s canvas. For example, if the image is 176x144, the DCT is applied to the central block of size N=128. Original Image DCT Select the 32 largest coefficients Modify the coefficients by using the signature IDCT Watermarked Image Figure 4. The embedding process of the method The next step of the technique after the DCT, is to select the 32 larger positive coefficients, in the low-mid frequency range. Robustness issues justify this choice, since the most important information of the image is there. The high frequency coefficients represent the image details and are vulnerable to most common image manipulation like filtering, compression etc. Of course one might argue that this is the place where changes that come from watermarking are more imperceptible, but this is true only if we re speaking of small sized blocks. Our scheme is applied to the whole image and since robustness is the main issue, the low and mid frequency coefficients are the most appropriate. The DC coefficient is not included in this search, because such a change would impose heavy altering to the image content. The selected coefficients c i are ordered by magnitude and then modified by the corresponding bit in the signature stream. If the i-th signature bit s(i) to be embedded is 1, a quantity D is added to the coefficient. This D quantity represents the persistence factor. If the watermark bit is 0, the same quantity is subtracted from the coefficient. Thus the watermarked coefficients * c i are given by Eq. 1. c * i = c i + f ( i) D (1) where + 1 f ( i) = { 1 if if s(i) = 1 s(i) = 0 and i = 1,2,3,...,32 Finally, the IDCT (Inverse Discrete Cosine Transform) is calculated to form the watermarked image. According to this method, the watermark is spread all over the image, which is a desired feature for the method s imperceptibility. TECHNICAL REPORT No. TR2000/10/01 5

6 4. Watermark Detection For the detection process, the original image is required. The extraction algorithm consists of the following steps (Fig. 5): 1. The forward DCT is applied to both images (the original and the image under testing) 2. The 32 largest DCT coefficients of the original image are located 3. The corresponding 32 DCT coefficients of the test image are located 4. Corresponding coefficients in both images are subtracted. Depending on the sign of the differences, a binary sequence is retrieved. A positive difference shows that the retrieved bit is 1 and a negative one shows that the bit is 0. Original Image DCT Selection of the coefficients Signature Extraction Test Image DCT Retrieval of corresponding coefficients Figure 5. The detection process of the method Screenshots A and B show the interaction for the embedding and detection programs respectively for an application example. The original sailboat image that was used in this test, along with it s watermarked version are shown in Fig. 6. The visual differences between the original and the watermarked images are hardly noticeable despite the large value of D used. This result was more or less expected, since the watermark was spread all over the image as explained in the section 3. The above observation can also be justified by the histogram of the difference image (Fig. 7). This image is the result of the subtraction between the original and the watermarked image. It must be noted, that a bias of 128 has been added, for better visualisation. This is why the histogram is centered around 128. The threshold value in screenshot A is the value of the last coefficient that was watermarked, the lowest one in order of magnitude. TECHNICAL REPORT No. TR2000/10/01 6

7 Input image name: sailboat Output image name: sailboat.wm100 Image height: 512 Image width: 512 FCT-- new N=512 Inserting watermark with a threshold of: 1131 Select persistence factor (5-100): 100 Job Completed Successfully Screenshot A. Embedding the watermark in the image Sailboat with dimensions 512x512 Input original image name: sailboat Input marked image name: sailboat.wm100 Image height: 512 Image width: 512 FCT-- new N= Watermark retrieved Job Completed Successfully Screenshot B. Detecting the watermark from the same image (a) Figure 6 : (a) Original Image (b) Watermarked with D=100 (b) TECHNICAL REPORT No. TR2000/10/01 7

8 Figure 7. Histogram of the Image of Differences 5. Experimental Results For the experiments of the proposed scheme, we have used both video frames and still images. The frames where of size 176x144 (Akiyo, Carphone, and Foreman). The still images used were sized 512x512 (Sailboat, Brandyrose, Sailboat, Opera, Papermachine). The watermark for the three smaller images was embedded for 3 different values of D (10, 50, 100). The still images were tested only for the value D=100. To test the watermark robustness, five common image-processing tasks were applied to each of the images with the help of Adobe Photoshop (Blurring, Gaussian Blurring, Uniform Noise, Sharpening and JPEG Compression). These tests are usually called attacks because their purpose is to destroy the watermark, making it illegible. Since the attacks alter the image s quality, it is important to emphasize that they should not be extreme because the image would then be destroyed. A severely damaged image, even without the watermark information present, is of no longer use to anyone! All results are presented in Table 1. TECHNICAL REPORT No. TR2000/10/01 8

9 Blurring Table 1. Experimental results Gaussian Blurring Uniform Noise Sharpening JPEG Compression Compression Ratio Akiyo (frame 100) Wm10 4,9:1 Wm50 4,2:1 Wm100 4,8:1 Carphone (frame 50) Wm ,4:1 Wm50 1 4,4:1 Wm100 4,4:1 Foreman (frame 100) Wm10 3,8:1 Wm50 3,8:1 Wm100 3,8:1 Sailboat Wm100 10,3:1 Brandyrose Wm100 24,5:1 Opera Wm100 17,2:1 Lochness Wm100 22,8:1 Papermachine Wm ,1:1 1: Only the last bit was lost 2: Two bits from the last 8 were lost 6. Discussion and Conclusions The main advantage of this method is that the DCT (forward or inverse), is applied to the whole image; thus the changes are spread throughout the whole image and they become almost undetectable. On the other hand, when the image is not an exact power of 2, a minor problem arises. If we use a large quantity for D (e.g. 100) in order to produce a robust watermark, when the inverse DCT is applied, the perimeter of the block that the DCT (forward and inverse) had been applied to, becomes apparent. TECHNICAL REPORT No. TR2000/10/01 9

10 The D value has to balance between the robustness of the watermark and the distortion of the image s quality. Large D values produce more robust watermarks but also deteriorate the image significantly. Small D values produce hardly noticeable distortions but are more fragile to most attacks. This is the necessary trade off between the image s quality and the watermark s robustness. Using small values of D (eg. D=10), the results show that the watermark did not make it through difficult tests-attacks such as the Gaussian Blurring and the addition of Uniform Noise. Table 1 shows, that some problems with Blurring, arose even for values of D=50 (for the image Carphone). Without a doubt, this is a strong attack, which distorts the image s quality a great deal. But practically, this image is of no longer use. This is clearly shown in the example images (Fig 8). (a) (b) Figure 8: (a) Original Image (b) Image with Gaussian Blur The fact that all images passed very easily the tests of Sharpening and JPEG Compression is very impressing and promising. It is also noticeable that the problems occur mainly for the video frames which are very small in size. Larger still images easily passed most of the tests (except of papermachine for gaussian blurring). That leads to the conclusion that the size of the image is important to the scheme. Of course larger values of D can be used in larger images since the distortion is spread in a wider area (so its not easily noticed) and so the method proves more robust there. The distortion for D=100 is less noticeable in a 512x512 image compared to a 176x144 video frame. That was the reason that the larger images were tested only for D=100. Another interesting fact is that the only still image where we encountered a problem was papermachine, a very special image, characterised by large homogeneous regions. This is also partly true for most of the video frames used. The compression ratios (CR) for the JPEG compression test, given in Table 1, are the maximum possible that Adobe Photoshop could produce. TECHNICAL REPORT No. TR2000/10/01 10

11 7. To probe further The main problem of this method concerns the dimensions of the bock to be transformed. If the dimensions are an exact power of 2 there is no problem. For all the other cases, a possible solution could be a clever algorithm. Depending on the image s dimensions, an adjusting could be made by adding or perhaps rejecting rows and columns. This could also appear helpful to possible attacks such as Resizing and Cropping. The whole issue is still under consideration. The method hasn t yet been tested against geometrical attacks such as Rotation or Shearing and others. A Print-Scan-Detect test would also be interesting. Also, the possibility of inserting multiple watermarks should be checked. References [1] J. Brassil et al. Document marking and identification using both lines and word shifting, Proc. Infocom95, IEEE CS Press, Los Alamitos, California, USA, [2] R. Barnett. Digital watermarking: applications, techniques and challenges, Electronics & Communication Engineering Journal, August 1999 [3] M. Kutter, F. A. P. Petitcolas. A fair benchmark for image watermarking systems, Electronic Imaging 99. Security and Watermarking of Multimedia contents, vol. 3657, Sans Jose, CA, USA, January [4] R. G. Van Schyndel, A. Z. Tirkel, C. F. Osborne. A digital watermark, Proc. IEEE International Conference on Image Processing, volume II, pp 86-90, Austin, Texas, [5] F. Hartung, B. Girod. Watermarking of uncompressed and compressed video, Signal Processing, 66(3): pp , May [6] W. Bender, D. Gruhl, N. Morimoto. Techniques for data hiding, In Proceedings of SPIE, volume 2420, p.40, February [7] I. Cox, J Kilian, T. Leighton, T. Shamoon. Secure spread spectrum watermarking for multimedia, Technical Report 95-10, NEC Research Institute, Princeton, NJ, USA, [8] M. Barni, F. Bartolini, V. Cappellini, A. Piva. A DCT-domain system for robust image watermarking, Signal Processing, 66(3): pp , May [9] J. K. O Ruanaid, T. Pun. Rotation, scale and translation invariant spread spectrum digital image watermarking, Signal Processing, 66(3): pp , May [10] D. Kundur, D. Hatzinakos. A robust digital image watermarking method using wavelet-based fusion, International Conference on Image Processing, pp , Santa Barbara, California, USA, October [11] D. Kundur, D. Hatzinakos. Digital watermarking using multiresolution wavelet decomposition, International Conference on Acoustic, Speech and Signal Processing (ICASP), volume 5, pp , Seattle, WA, USA, May TECHNICAL REPORT No. TR2000/10/01 11

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