1. DR. TAMARA CHER R. MERCADO University of Southeastern Philippines Institute of Computing 《 Models of Watermarking 》

2. Contents 3.1 Communications 3.2 Communication-based models of watermarking 3.3 Geometric models of watermarking 3.4 Basics of Digital Image 3.5 Image Watermarking Example

3. 3.1 》Communications Components of Communication System Fig. 3.1 Standard model of a communication system m: the message we want to transmit x: the codeword encoded by the channel encoder n: the additive random noise y: the received signal mn: the received message

7. The encrypted message (ciphertext) is transmitted over the channel

9. prevent passive attacks in the form of unauthorized reading of the message.

10. prevent active attacks in the form of unauthorized writing.

11. Downside:

12. does not necessarily prevent an adversary from knowing that a message is being transmitted.

14. Modulation is done according to a secret code, which spreads the signal over a wider bandwidth than required

16. Spread spectrum guarantees delivery of signals. Cryptography guarantees secrecy of messages. It is thus common for both technologies to be used together.

18. Ways to incorporate the cover Work into the traditional communications modelThe cover Work is considered purely as noise (Basic Model). The cover Work is still considered noise, but this noise is provided to the channel encoder as side information. Cover Work is not considered as noise, but rather as a second message that must be transmitted along with the watermark message in a form of multiplexing.

23. A model of watermarking that allows wato be dependent on co.

24. The model is almost identical to Blind Detector, with the only difference being that co is provided as an additional input to the watermark encoder.

25. Allows the embedder to set cw to any desired value by simply letting wa = cw − co

27. The two messages, co and m, will be detected and decoded by two very different receivers: a human being and a watermark detector, respectively.

29. Graphic/Image File Formats Graphic/Image Data Structures Pixels: picture elements in digital images Image Resolution:number of pixels in a digital image (Higher resolution always yields better quality.) Bit-Map: a representation for the graphic/image data in the same manner as they are stored in video memory. 3.3 》Geometric Models of Watermarking

31. Region of acceptable fidelity: a region in which all works appear essentially identical to a given cover work

32. Detection region: describes the behavior of the detection algorithm

33. Embedding distribution or embedding region: describes the effect of an embedding algorithm

35. Types of Digital Image Binary Image Each pixel is stored as a single bit (0 or 1) A 512×512 monochrome image requires 32.768 kB of storage. 3.4 》Basics of Digital Image 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 0 1

36. Graphic/Image File Formats Gray-scale Images Each pixel is a shade of gray, from 0 (black) to 255 (white). This range means that each pixel can be represented by eight bits, or exactly one byte. A 512×512 grayscale image requires 262.14 kB of storage. 3.4 》Basics of Digital Image 138 201 90 128 345 95 200 122 112 78 21 198 56 90 1 0 0 0 1 0 1 0

37. Graphic/Image File Formats True Color or RGB (Red-Green-Blue) Each pixel has a color described by the amount of red, green and blue in it. Has a total of 256x256x256 = 16,777,216 different possible colors in the image 24 bit images: total number of bits required for each pixel. A 640×480 24-bit color image would require 921.6 kB of storage 3.4 》Basics of Digital Image

38. Graphic/Image File Formats True Color or RGB (Red-Green-Blue) 3.4 》Basics of Digital Image

39. Graphic/Image File Formats Indexed Each pixel has a value which does not give its color (as for an RGB image), but an index to the color in a color map. Color map or color palette is associated with the image which is simply a list of all the colousused in that image. Compuserve GIF allows only256 colors or fewer in each image and so its index values only requires one byte each. 3.4 》Basics of Digital Image

40. Graphic/Image File Formats 3.4 》Basics of Digital Image Indexed Pixels labeled 5 correspond to 0.2627 0.2588 0.2549, which is a dark grayish color.

41. The LSB Technique 3.5 》Image Watermarking Example LSB: Least Significant Bit Considered as the simplest technique for watermark insertion. For a 24-bit image, each pixel has 3 bytes and each color (RGB) has 1 byte or 8 bits in which the intensity of that color can be specified on a scale of 0 to 255. A bright purple in color would have full intensities of red and blue, but no green. This pixel can be shown as X0 = {R=255, G=0, B=255} Now let’s have a look at another pixel: X1 = {R=255, G=0, B=254}

42. The LSB Technique 3.5 》Image Watermarking Example Since this difference does not matter much, when we replace the color intensity information in the LSB with watermarking information, the image will still look the same to the naked eye. Thus, for every pixel of 3 bytes (24 bits), we can hide 3 bits of watermarking information, in the LSBs. A simple algorithm for this technique would be: Let W be watermarking information For every pixel in the image, Xi Do Loop: Store the next bit from W in the LSB position of Xi [red] byte Store the next bit from W in the LSB position of Xi [green] byte Store the next bit from W in the LSB position of Xi [blue] byte End Loop

43. The LSB Technique 3.5 》Image Watermarking Example W = TAMMY 01010100 01000001 01001101 01001101 01011001 49 – 110001  110000 64 – 1000000  1000001 66 – 1000010  1000010 55 – 110111  110111 76 – 1001100  1001100 80 – 1010000  1010001 56 – 111000  111000 82 – 1010010  1010010

44. The LSB Technique 3.5 》Image Watermarking Example 49 – 110001  110000 (48) 64 – 1000000  1000001 (65) 66 – 1000010  1000010 (66) 55 – 110111  110111 (55) 76 – 1001100  1001100 (76) 80 – 1010000  1010001 (81) 56 – 111000  111000 (56) 82 – 1010010  1010010 (82) 48 55 56 65 76 82 66 81

45. The LSB Technique 3.5 》Image Watermarking Example Watermark Extraction take all the data in the LSBs of the color bytes and combine them. This technique of watermarking is invisible, as changes are made to the LSB only, but is not robust. Image manipulations, such as resampling, rotation, format conversions and cropping, will in most cases result in the watermark information being lost.