OVERVIEW OF DIGITAL WATERMARKING CATEGORIES AND TECHNIQUES

OVERVIEW OF DIGITAL WATERMARKING CATEGORIES AND TECHNIQUES Nurul Badriah Binti Abu Bakar 1, Mazleena Binti Salleh 1, Subariah Binti Ibrahim 1 1 Department of Computer System and Communications, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia Email: nbadrya@gmail.com 1, mazleena@utm.my 1, subariah@utm.my 1 ABSTRACT Nowadays technologies make data sharing and information searching became easier but unfortunately copyright violation and data duplication by unauthorized and irresponsible person cause the authorized author reluctant in sharing their precious information to public. To overcome this problem various techniques proposed by previous researchers and one of the famous and efficient approach among researchers is digital watermarking. Digital watermarking is the process of embedding data into multimedia objects. Thus in this paper, an overview of digital watermarking s categories and corresponding techniques are discussed in detailed. Keywords: Digital watermarking, spatial, frequency, least significant bit (LSB), discrete cosine transform (DCT), discrete wavelet transform (DWT). 1 INTRODUCTION The technology of computers are getting advance year by year. Internet is one of the highest technologies that have been used for various purposes, such as for sending, transferring and publishing data or information to all over the world. Ultimately, these technologies are really helpful to ease up routine activity. Unfortunately, the usage of the internet had been misused by irresponsible person for their own benefits or with intention of sabotage. These types of irresponsible person are often referred as hackers, they will duplicates and distribute secure data or information without permission of the owner. This is the biggest constraint that cause most of authors, publishers, photographers and others user to feels reluctant of sharing or distributing their data through the internet. Misuses of this precious data through the internet without permission will directly breach the owner copyright and the integrity of their data. This is very serious and in order to overcome this tough issue, digital watermarking techniques have been proposed by many researchers. Digital watermarking consists of two categories which contains various techniques such as Least Significant Bit (LSB), Spread Spectrum (SS), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Discrete Fourier Transform (DFT) and so on. In [1], digital watermarking is described as an approach to protect copyright against unauthorized uses of multimedia contents such as illegal copy, distribution, and forgery and so on. The researcher [1] claimed that any insertion and extraction of the copyright information which is referred as watermark in digital contents can be used to prove the ownership of the copyright holder. Therefore, in this paper digital watermarking techniques will be discussed in detail. This paper is organized as follows. Section 2 is brief explanation on types of digital watermarking such as Human Perception and Working Domain and techniques under their categories. Section 3 is discussed about methodology of watermarking techniques. Section 4 has shown the experimental results. Conclusion and future work is offered in section 5. 2 DIGITAL WATERMARKING Digital watermarking is a technique that embedding information into digital images which can be extracted later for the purposes of ownership verification. For example, information that often been used are such as owner name, owner address, status and destination of the host data. Furthermore, digital watermarking has been used for monitoring, content indexing and auditing the data as well. With the uses of watermarking technique, the source and destination of the data can be easily identified and thus facilitates in tracing the case of suspected copyright violation [3]. Digital watermark techniques can be categorized in terms of Human III-1

III-2 The 6 th International Conference on Information & Communication Technology and Systems Perception and Working Domain. Figure 1 illustrates the division of categories in digital watermarking and techniques that contained under corresponding categories. Visible Fragile Human Perception Invisible Semi-Fragile Digital Watermarking Robust 2.1 Human Perception Spatial LSB Figure 1: Types of watermarking Techniques Working Domain DCT Frequency DWT Human perception can be described as digital watermarks that can be recognized by observation and from the view of human perception, watermark can be clustered into two types, which are visible watermarks and invisible watermarks. Visible watermarks are visual patterns like logos, which inserted into images or video, for example is TV3 logo that displayed at top corner of their commercial video channel. In contrast, invisible watermarks are digital watermarks that are not visible on normal human. Normal human s observation is unable to distinguish between the original data and watermarked data. Furthermore, from the robustness point of view proposed by Santoso [4], invisible watermark can be divided into three characteristics as follows: i. Fragile: Watermarks that designed to be very sensitive, as it can be destroyed easily even by the slightest modifications that are normally performed to prepare data before publication. This characteristic is meant to indicate whether any intentional modification have been made to the data or not, or to identify the area of modification. ii. Robust: Watermark that designed to be robust against any attacks that can destroy or remove it. Even though, it had been attack, the watermarks still exist as before. iii. Semi-fragile: Watermarks that combines the characteristics of fragile and robust watermarks. This type of watermarks is resilient against to the attacks just like robust watermarks. It is also able to identify the altered area just like fragile watermarking. This type of watermarks is capable to distinguish between any altered and unaltered area. 2.2 Working Domain There are two working domain in watermarking techniques which are spatial and frequency. According to Lee and Jung [5], spatial method analyzes a data from the spatial point of view, watermarking method based on the spatial domain will scatters information to be embedded to make the information hardly detectable. It uses minor changes of the value of pixel directly. This domain is relatively easy to implement, but it is weak in geometric signal manipulations such as rotation, scale and translation [6]. Frequency domain is also referred as transform domain by some researcher. According to Lee and Jung [5], frequency domain uses the methods of data transformation to embed and also extract watermark. In this method, watermark that want to be embedded will be distributed in overall domain of an original data and once embedded the watermark is hard to be deleted. This makes frequency domain a way better than spatial domain because spatial domain watermarking techniques are usually less robust to attacks such as compression and noise [7]. Major advantage of frequency domain is the robustness to most common signal manipulations [6]. Least Significance Bit (LSB) and Spread Spectrum (SS) are techniques that developed for spatial domain. There are also various types of techniques that rely under frequency or transform domain such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Discrete Fourier Transform (DFT), and Fast Fourier Transform (FFT). Next section 2.3 was discussed on some techniques in detail. 2.3 Digital Watermarking Techniques There are numerous of techniques for digital watermarking. However, in this paper only three techniques are discussed in details which are Least Significance Bit (LSB), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT).

Overview Of Digital Watermarking Categories and Techniques - Nurul Badriah Binti Abu Bakar III-3 2.3.1 Least Significant Bit (LSB) LSB is the most straightforward technique for embed watermark into an image. It is sometimes referred as the right-most bit, due to the convention in positional notation of writing less significant digit further to the right [7]. It is analogous to the least significant digit of a decimal integer. Figure 2 shows the binary representation of decimal 149 with LSB being highlighted. The MSB (Most Significant Bit) in 8-bit binary number represents a value of 128 decimal and LSB represents the value of 1. Figure 2: Binary Bit 2.3.2 Discrete Cosine Transform (DCT) DCT expresses a sequence of finitely many data points in terms of a sum of cosine functions oscillating at different frequencies. DCTs are important to numerous applications in science and engineering, from the compression of audio and images (where small high-frequency components can be discarded), to spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical in these applications for compression, cosine turns out to be much more efficient (as explained below, fewer are needed to approximate a typical signal), whereas for differential equations the cosine express a particular choice of boundary conditions. DCT is using DCT coefficient mask as shown in Figure 3. These coefficients are divided into three sub-band that use low band, mid-band and high band. techniques are in the same domain, their technique for embedded and extraction processes are different. DWT uses Haar transformation as fundamental for wavelet. DWT [8] on the treated image gives the following two decompositions where the first component consists of MRA and another component is consists of Multi-Resolution Representation (MRR). Figure 4 illustrates Haar Transformation of 2-D image. As seen in Figure 4, the MRA component represents the part of LL 2 with a half resolution and MRR components represent the different information on the parts which are HL 1, HH 1 and LH 2, where HL 1 signifies the vertical direction, HH 1 the diagonal direction and LH 2 signifies the horizontal direction. In other words, MRA component represents the low frequency component and MRR the high frequency component. It is also possible to repeat DWT to MRA components so this component decomposes of two components as before. Repeating DWT forms a hierarchical structure in the image which makes various resolution levels embedded digital watermarks possible. The Haar base is used as main wavelet of DWT. Figure 4: Haar Transformation of 2-D image [8] 3 RESULT In this section, experimental result from three techniques that have been tested which are Least Significance Bit (LSB), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are explained. Figure 3: DCT coefficient mask [13] 2.3.3 Discrete Wavelet Transform (DWT) DWT is a form of frequency domain which similar to DCT. Even though both of these 3.1 Effect of Embedded Watermark For this testing, there are three type of images have been used which are images with Bitmap (.bmp), Portable Network Graphic (.png) and Tagged Image (.tiff). The effect after embedded process will be discussed further in Section 5.1.1, 5.1.2 and 5.1.3.

III-4 The 6 th International Conference on Information & Communication Technology and Systems 3.1.1 Effect of Embedded Watermark on Bitmap (.bmp) The test image for bitmap format (.bmp) is a grayscale 8-bit Lena image which is 512 512. The watermark used for watermarking is a 50 20 2-bit grayscale. Figure 5 shows watermarked image and also histogram for the watermarked image. These images have been tested by using three techniques which are LSB, DCT and DWT. (1a) (1b) impact after embedded process. As seen in Figure 5 (2b) wave of the histogram is the highest compared to other histograms, this is because LSB algorithm will change each right-most bit of the image. The exchanging of the value is depends on bit value, if the right-most value is 0 then it will be changed to 1 and vice versa. 3.1.2 Effect of Embedded Watermark for Portable Network Graphic (.png) For the Portable Network Graphic (.png) format, the test image is also a grayscale 8-bit and the image size is 512 512. The watermark used for watermarking is a 50 20 2-bit grayscale. Watermarked image and the histogram result for this format image had been shows in Figure 6. This format also had been tested using three techniques (LSB, DCT and DWT). (2a) (2b) (1a) (1b) (3a) (3b) (2a) (2b) (4a) (4b) (3a) (3b) Figure 5: Effect of embedded watermark for bitmap format image (.bmp) (1a) Original Image (1b) Histogram for original image (2a) Watermarked image for LSB (2b) Histogram for LSB (3a) Watermarked image for DCT (3b) Histogram for DCT (4a) Watermarked image for DWT (4b) Histogram for DWT As seen in Figure 5, there are effects on each image, but by observing watermarked images the differences were hardly recognized clearly by human eyes due to the effect area is too small to be spotted. Thus the differences were observed by using histogram, so from the histogram in Figure 5 (2b, 3b and 4b), we can see each techniques gave an effect. LSB technique was obviously result an (4a) (4b) Figure 6: Effect of embedded watermark for format image PNG (1a) Original Image (1b) Histogram for original image (2a) Watermarked image for LSB (2b) Histogram for LSB (3a) Watermarked image for DCT (3b) Histogram for DCT (4a) Watermarked image for DWT (4b) Histogram for DWT The result for this type of format is same like bitmap format. As seen in Figure 6 (2b, 3b and 4b),

Overview Of Digital Watermarking Categories and Techniques - Nurul Badriah Binti Abu Bakar III-5 LSB still the best techniques. In addition, the histogram from DCT technique is lowest than the other. 3.1.3 Effect of Embedded Watermark for Tagged Image File Format (.tiff) This image is also a grayscale 8-bit, watermark used for watermarking is a 50 20 2-bit grayscale. Figure 7 shows images and histogram results after the embedded process by using different techniques (LSB, DCT, and DWT). 3.2 Performance Peak Signal to Noise Ratio (PSNR) used to measure the performance of each technique. It will calculate the noise between original image and watermarked image. This measurement is based on PSNR equation (1). (1) (1a) (2a) (1b) (2b) Where, I and I are the original and the noise images. Furthermore, the time for embedded and extracted process had been record as well. 3.2.1 Embedded Time Performance As shown in Figure 8, embedded time performance for three techniques (LSB, DCT and DWT) using three type of format image (.bmp,.png and.tiff) can be seen. As seen LSB technique is the faster technique to perform embedded process. The performance time value for LSB is less than DCT and DWT technique. Based on type of image format, it shows that bitmap (.bmp) format performs faster while using LSB and DWT technique. Anyway,.tiff format performs faster while using DCT technique. (3a) (3b) (4a) (4b) Figure 7: Effect of embedded watermark for format image TIFF 1a) Original Image (1b) Histogram for original image (2a) Watermarked image for LSB (2b) Histogram for LSB (3a) Watermarked image for DCT (3b) Histogram for DCT (4a) Watermarked image for DWT (4b) Histogram for DWT Same as.bmp and.png format, the image cannot be trace by human eye. The effect for this image can be observed using histogram shows Figure 7(1b, 2b, 3b, 4b). As seen, LSB technique gave good result; DWT technique histogram wave is different from the original histogram. It shows that DWT technique also gave an impact to the image. Figure 8: Embedded Time Performance 3.2.2 Extraction Time Performance Figure 9 shows the extracted time of performance for three techniques (LSB, DCT and DWT) using three type of format image (.bmp,.png and.tiff). It shows that LSB is the faster technique to perform extracted process follow by DCT, while DWT takes long time for extracted process. Based on type of image format, it shows that bitmap format performs faster when extracted process for all techniques. Figure 9: Extracted Time Performance

III-6 The 6 th International Conference on Information & Communication Technology and Systems 3.2.3 PSNR Performance Based on Figure 10, it shows the PSNR performance for three techniques (LSB, DCT and DWT) using three type of format image (.bmp,.png and.tiff). By observing Figure 10 graph, it shows that the PSNR value for LSB technique is lowest than other techniques. That make the LSB technique is the better technique for embedded the watermark. The lowest PSNR, make it high imperceptibility. Bitmap image have the lowest value of PSNR for LSB technique and DWT technique while, for DCT technique,.tiff had the lowest PSNR. 4 CONCLUSION AND DISCUSSION As seen in experimental result, it shows that type of image format also give affect embed and extract process. As we can see from the result, Bitmap (.bmp) format image is performed faster than Portable Network Graphic (.png) and Tagged Image File Format (.tiff). Furthermore, it also shows that all techniques can embed and extract back the watermark. But, these techniques only for works for digital images, it does not perform well when it comes to printed document. Even though living in an electronic age as today, the usage of printed documents still cannot be disregarded. Certificates, academic transcripts, wills, contracts and land titles are example of personal and private printed documents. As the future work, a study on Discrete Fourier transforms technique will be conducted to perform watermark on document in order to improve the accuracy of extracted watermark. REFERENCES Figure 10: PSNR Performance [1] Lee, D., Kim, T., Lee, S. and Paik, J. (2006). A Robust Watermarking Algorithm Using Attack Pattern Analysis. ACIVS 2006, LNCS 4179, p.757-766, Springer-Verlag, Berlin Heidelberg. [2] Solanki, K., Madhow, U., Manjunath, B.S., Chandrasekaran, S., El-Khalil, I. (2006).Print and Scan Resilient Data Hiding in Images. IEEE Trans. Information Forensics and Security 1(4), 464 478 [3] De Vleeschouwer, C. Delaigle, J.F. and Macq, B., (2002). Invisibility and Application Functionalities in Perceptual Watermarking-An Overview. Proceeding of the IEEE. January 2002. Vol.90, No.1, p.64-77. [4] Santoso, P. (2004). Digital Watermarking as Content Protection Scheme. Jurnal Teknik Elektro Vol 4, No.1, Maret 2004 : 53-62. [5] Lee, S.J. and Jung, S.H. (2001). A Survey of Watermarking Techniques Applied to Multimedia. ISIE, Pusan, Korea, p. 272-277. [6] Furht,B. and Kirovski,D. (2006). Multimedia Watermarking Techniques and Applications. Boca Raton, FL: Auerbach Publcation. [7] Shih, F., Y. (2008).Digital Watermarking and Steganography: Fundamentals and Techniques. Boca Raton, FL: CRC Press. [8] Takai,R and Nagasaka,K.(2003).Digital Watermarks Using Discrete Wavelet Transformation and spectrum spreading. Cybernetics and informatics, vol1.1, no.6. [9] Yoo, H., Lee,K., Lee, S. and Lim, J.,(2002). Offline Authentication Using Watermarks. ICICS 2001, LNCS 2288, p. 200-213, Berlin: Springer-Verlag. [10] Solanki, K., Madhow,U., Manjunath, B.S., Chandrasekaran, S. (2005).Modeling the Print- Scan Process for Resilient Data Hiding, Steganography, and Watermarking of Multimedia Contents VII, Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 5681, pp.418-429. [11] Xu, H. and Wan, X. (2008). Watermarking Algorithm for Print-scan Based on HVS and Multiscale Error Diffusion, International Conference on Computer Science and Software Engineering. 2008. p. 245-248 [12] Solanki, K., Madhow, U., Manjunath, B.S., Chandrasekaran, S. (2004).Estimating and Undoing Rotation for Print-scan Resilient Data Hiding. In: IEEE International Conference on Image Processing, vol. 1, pp. 39 42. [13] Emek,S. and and Pazarci,M. (2002).A Cascade DWT-DCT Based Digital Watermarking Scheme.