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EE 5359Fall 2010 PROJECT PROPOSAL DIGITAL WATERMARKING Abrar Ahmed Syed 1000 61 4216 Under the guidance of Dr. K. R. Rao.

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Presentation on theme: "EE 5359Fall 2010 PROJECT PROPOSAL DIGITAL WATERMARKING Abrar Ahmed Syed 1000 61 4216 Under the guidance of Dr. K. R. Rao."— Presentation transcript:

1 EE 5359Fall 2010 PROJECT PROPOSAL DIGITAL WATERMARKING Abrar Ahmed Syed 1000 61 4216 Under the guidance of Dr. K. R. Rao

2 Key Points What is digital watermarking ? Why do we need digital watermarking ? What are the different types of watermarking ? What are its characteristics and requirements ? What are the different techniques and schemes used to watermark ? Two forms of frequency domain watermarking techniques in detail.

3 Key Points (Contd.) What are the different types of attacks it is susceptible to ? One attack implementation ? What are the ways of counter-attacking a watermarking attack ? What are the different laws and principles governing watermarking ? What are its drawbacks ? What is its future perspectives ?

4 Watermarking Embedding a digital signal (audio, video or image) with information which cannot be removed easily is called digital watermarking. Figure 1 shows the block diagram of embedding digital watermark. Figure 1: Block diagram of embedding digital watermark Attacking Function Detecting and Retrieving Function Signal Any Embedding Function ‘E’ Signal

5 Watermarking As advances are made in field of communication, it became necessary to cipher and decipher. This led to discovery of stenography and watermarking. Stenography: Hiding information over cover. Watermarking: Hiding information related to cover. Embedding is done by manipulating contents in the signal itself and is made imperceptible.

6 Encoding For example sake, figure 2 shows the block diagram for audio watermark encoding. Figure 2: Encoding block diagram of audio watermarking technique Framing Spectral Analysis DC Carrier Removal Watermark Addition Original Signal Watermarked Signal

7 Decoding For example sake, figure 3 shows the block diagram of audio watermark decoding. Figure 3: Decoding block diagram of audio watermarking. Framing Spectral Analysis Watermark Processing Watermarked Signal Original Signal

8 Applications? Ownership assertion: Watermarking is used to establish ownership over the content Fingerprinting: In fingerprinting, watermarking is used to avoid illegal distribution of media publicly Authentication and integrity verification: Content which is protected by key verification should not be accessible without authentication.

9 Applications? Content labeling: Bits embedded in data giving extra information Usage control: To limit copies creation of copyrighted data, by blocking using watermark. Content protection: Visible watermark block is used for this purpose. No universal technique to satisfy all of these. (Source: [2] Memon, N., & Wong, P. W. (1998). Protecting digital media content. Communications of the ACM, 41(7), 35-43.)

10 Classification or Types Visible: Any text or logo to verify or hide content F w =(1-α)F+ α*W [12] F w = Watermarked Image α= constant 0<= α<=1 (If 0, no watermark or if 1, watermark is present) F=original image W=watermark # Watermarking done by using Bytescout watermarking software [20]

11 Classification or Types Invisible: Hidden in the signal or content. Cannot be perceived by human eye or ear. Usually used for authentication or security Robust: Cannot be manipulated without disturbing the host signal. # Watermarking done by using Bytescout watermarking software [20]

12 Classification or Types Fragile: Fails with even the slightest mod. Public: Resists benign transformation but fails malignant ones. Capacity: Based on length of the embedded signal - Zero-Bit: It denotes 1 or 0 based on whether watermark is present or absent. Also called Italic zero bit. - N-Bit: Its N-Bit long. (m=m1, … m=mn, with n = | m | ) or M = {0,1} n Is modulated. Also called non-zero bit. [1]

13 Classification or Types Perceptibility: If the presence of watermark is evident in the host signal Imperceptibility: If original host and marked host are indistinguishable.

14 Classification or Types Perceptible and Imperceptible Example [15] (a) Original Image(b)Watermark Image (c) Perceptible Image(d)Imperceptible Image

15 Classification or Types Embedding techniques: -Spread spectrum: In this case, watermark is obtained by additive modification. It is robust. It has low information capacity due to host interference. -Quantization: Marking is done by quantizing. It is low robust. It has more data capacity -Amplitude modification: In this marked signal is embedded by additive modification. Similar to spread spectrum but is particularly done in spatial domain.

16 Techniques and Schemes Spatial domain: In this technique randomly selected subsets are modified. It is not reliable when subjected to filtering or lossy compressions - LSB coding: Least significant bit is substituted with watermark - Predictive coding: Co-relation between adjacent images is used. It can be further improved by adding constant to difference in adjacent values.

17 Techniques and Schemes - Correlation based: P seudo random noise (PN) with a pattern W(x, y) is added to an image according to I w (x,y)=I(x,y)+ K * W(x,y) [12] I w (x,y) = Watermarked image I(x,y)=Original image k=gain factor At the decoder the correlation between the random noise and the image is found. If the value exceeds threshold, watermark is detected.

18 Techniques and Schemes -Patchwork: First ever watermarking scheme. Image divided into two subsets. An operation is applied to these two subsets in the opposite direction. If a[i] is the value of a sample at I in subset ‘A’ which is increased and b[i] is the value of the same sample in the subset ‘B’ which is decreased, then the difference ∑a[i]-b[i]= 2N for watermarked images = 0 otherwise 1<=N<=∞

19 Techniques and Schemes Frequency domain: Values of lower frequency coefficients are altered. This technique is applied to the whole image. - Discrete cosine transform (DCT): Converts data in spatial domain into cosine with different amplitudes in frequency domain. [11][12][18] - Discrete wavelet transform (DWT): It decomposes signal into set of basic wavelets. Lower frequencies are then altered at different resolutions [11][12][18]

20 Attacks “Digital watermarking is not as secure as date encryption. Therefore, digital watermarking is not immune to hacker attacks”.[5] The following are few forms of attacks Basic: Take advantage of limitations in design of embedding technique Robustness: Attempts to diminish or remove presence of watermark Presentation: Modifies the content of the host signal to detect watermark

21 Attacks Intrepretation: They find a situation where ownership certification is prevented. Implementation: Attacks the detection software. Removal: Includes denoising, lossy compression, quantization, remodulation, collusion and averaging attack. Removes watermark from cover signal. Geometrical: Instead of removing they distort the watermark using spatial or temporal alteration of stego data.

22 Attacks Cryptographic: Brute force attacks are used for exhaustive search. Protocol, estimation, perceptual remodulation, copy and benchmarking Wavelet Based - Active: Hacker removes or spoils watermark - Passive: Hacker just identifies, does not damage - Forgery: Forges new watermark

23 Attacks Wavelet based - Collusion: Hacks different copies with different watermarks and joins them to make one single watermark. - Distortive: Hacker applies distortive transformation to make the watermark undetectable

24 Counter - Attacks Power spectrum condition (PSC) - When original signal is given, its power spectrum can be varied. The PSC can be shaped to resist estimation based attack. Noise visibility function (NVF) - Watermark is added in the form of noise. De-noising is used to derive noise. This noise becomes the watermark.

25 Privacy Laws Privacy by design Avoid embedding independently useful identifying information directly in watermark Provide notice to end users Control access to reading capability Respond appropriately when algorithms are compromised Provide security and access controls for back ‑ end databases Limit uses for secondary purposes Provide reasonable access and correction procedures for personally identifiable information

26 Project Goal An image ‘A’ will be selected from a set of images of various sizes and color scales. A watermark ‘B’ will be selected from a set of images of various sizes and color scales. The MSB of ‘B’ will be read and will be written on the LSB of ‘A’. Thus, ‘A’ will be watermarked with ‘B’ resulting in a combined image ‘C’. The technique used will be LSB technique. LSB technique is a form of Spatial Domain Technique. Noise will be added to image ‘C’ resulting in image ‘D’. Both the images ‘C’ and ‘D’ will be compressed. The compression will be done in the following steps. The image is broken into 8X8 blocks of pixels. [19] From left to right and top to bottom discrete cosine transform is applied to each block Blocks are compressed through quantization. For reconstruction of image, decompression is done by inverse discrete cosine transform. [11][12] Once this is done, the whole process will be reversed, i.e. The compressed image will be decompressed, the noise will be removed, the watermark will be removed, and the original watermark and the original image will be obtained. An analysis will be done on the original image before starting of any process and the final retrieved image at the end of the entire process. The tools used in this project’s implementation will be Matlab, Visual Studio, Stirmark and Bytescout. [20]

27 References [1] R. Popa, “An analysis of steganographic techniques”, The Politehnica University of Timisoara, Faculty of Automatics and Computers, Department of Computer Science and Software Engineering, Website: http://ad.informatik.uni-, 1998 [2] N. Memon and P. W. Wong, “Protecting digital media content”, Communications of the ACM, Vol. 41(7), pp 35-43, 1998 [3] T. C. Lin and C. M. Lin, “Wavelet based copyright protection scheme for digital images based on local features”, Information Sciences: an International Journal, Vol. 179(19), Sept. 2009. [4] G. Langelaar, I. Setyawan and R.L. Lagendijk, “Watermarking digital image and video data”, IEEE Signal Processing Magazine, Vol. 17, pp 20-43, Sept. 2000 [5] M. Yeung, B. Yeo and M. Holliman, “Digital watermarks: shedding light on the invisible”, IEEE Micro, Vol. 18(6), pp 32-41. Nov. 1998 [6] P. Vidysagar, S. Han, and E. Chang. "A survey of digital image watermarking techniques”, 3rd IEEE International Conference on Industrial Informatics (INDIN 2005), edited by Dillon, T., Yu, X. and Chang, E., pp 495-502, Perth, Western Australia, 2005

28 References [7] S. Voloshynovskiy, S. Pereira and T. Pun, “Attacks on digital watermarks: classification, estimation-based attacks and benchmarks,” IEEE Commun. Mag., Vol. 39(8), pp. 2–10, Aug. 01 [8] X. Jian-hui, W. Li-na and Z. Huan-guo, “Wavelet based denoising attack on image watermarking”, Wuhan University Journal of Natural Sciences, Vol.10(1), pp. 279-83, Oct. 2005 [9] A. Khan and A.M. Mirza, “Genetic perceptual shaping: utilizing cover image and conceivable attack information during watermark embedding”. Inf. Fusion 8, 4, 354-365, Oct. 2007 [10] I. J. Cox, J. Kilian, T. Leighton and T. Shamoon, “Secure spread spectrum watermarking for multimedia”, IEEE Transactions on Image Processing, Vol. 6(12), pp 1673–1687, 1997 [11] N. Ahmed, T. Natarajan, and K. R. Rao, "Discrete cosine transform", IEEE Trans. Computers, Vol. 23(1), pp. 90-93, Jan. 1974. [12] V. Britanak, K. R. Rao and P. Yip, “Discrete Cosine Transform: Properties, Algorithms, Advantages, Applications”, Academic Press Publications, ISBN 978-0-12-373624-6, Boston, 1990. [13] M. Arnold, M. Schmucker and S. D. Wolthusen. “Techniques & Application of Digital Watermarking and Content Protection”, Artech House Publications, ISBN:1580531113, Boston, London, 2003

29 References [14] I. J. Cox, M. L. Miller, J. A. Bloom, J. Fridrich and T. Kalker, "Digital Watermarking and Steganography" (Second Edition), Morgan Kaufmann Publications, ISBN: 9780123725851, 2008 [15] Jeng-Shyang Pan, Hsiang-Cheh Huang and L. C. Jain, “Intelligent Watermarking Techniques- Series on Innovative Intelligence, Vol. 7”, World Scientific Publication, ISBN:9812387579, 2004 [16] F. Jin, P. Fieguth, L. Winger, and E. Jernigan, “Adaptive Wiener filtering of noisy images and image sequences,” Proc. IEEE Int. Conf. on Image Process, pp. 349–352, Sept. 2003 [17] F. Mintzer and G. W. Braudaway, “If one watermark is good or more better?” IEEE, Int. Conf. on Acoustics, Speech and Signal Processing- ICASSP, pp. 2067–2070, 1999 [18] J. Cummins, P. Diskin, S. Lau and R. Parlett, “Stegnography and digital watermarking”, School of Computer Science, The University of Birmingham, 2004 Website: df [19] Electrical and Computer Engineering, University of Victoria, British Columbia. Website: [20] Bytescout watermarking software for visible and invisible watermarking. Website:

30 THANK YOU QUESTIONS & SUGGESTIONS Who questions much, shall learn much & retain much -Francis BaconFrancis Bacon Questions are guaranteed in life, answers aren’t. – English Proverb

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