1. Multimedia Network Security Lab. On STUT Adaptive Weighting Color Palette Image Speaker:Jiin-Chiou Cheng Date:99/12/16

2. Multimedia Network Security Lab. On STUT 2 Outline Introduction Relative works Proposed scheme Diagram Experimental results Discussion

3. Multimedia Network Security Lab. On STUT 3 Introduction Recently, researchers pay more attention to consider how to provide the photo’s safety and copyright. In the paper, we let distinct color palette to be embedded in cover-image according to user’s authority. The user will recover the cover image more clearly with high authority. Besides, we provide a adaptive data hiding scheme to embed the color palette into cover-image.

4. Multimedia Network Security Lab. On STUT Relative works Chaumont-Puech’s scheme Chaumont M. and Puech W., “A Color Image Hidden in a Grey-Level Image,” in ISandT Third European Conference on Colour in Graphics, Imaging, and Vision, CGIV’2006, pp. 226–231., 2006. Ni Z. et al. reversible data hiding scheme Ni Z., Shi Y. Q., Ansari N., and Su W. “Reversible Data Hiding,” IEEE Trans. on Circuits and System for Video Technology, Vol.16, pp.345-362, 2006. 4

5. Multimedia Network Security Lab. On STUT Relative works -- Chaumont-Puech’s scheme Input: The color image as cover-image. Output: The stego-image. (1). Find out the non-repeat pixels in the color image. (2). Use the ISO-DATA K-means [1] algorithm to get K pixels for color representation. The training formula is following: where I is a color image of dimension N pixels, C(k) is the k th color of the research K colors, dist is a distance function to calculate the E uclidean distance between I and C(k), and Pı,k  {0,1} is the membership value of pixel ı to color k. (3). Use Layer Running [4-6] algorithm to get new reordering palette and new index grayscale image. (4). Embed the color image’s palette information into the grayscale image’s LSB. 5

6. Multimedia Network Security Lab. On STUT Relative works -- Ni Z. et al. reversible data hiding scheme Input: Cover-image Output: Stego-image (1). Find out the peak point (pixel value is 2) and the zero point (pixel value is 6) shown as Fig.(a). (2). The value of pixels between 3 and 5 is incremented by “1”, i.e., shifting the range of the histogram, [3 5] to the right-hand side by 1 unit and leaving the grayscale value 3 empty shown as Fig.(b). (3). Embed the secret data “0” and “1” into the grayscale value of 2 and 3, respectively. The stego-image’s histogram is shown as Fig.(c). 6 (a) (b)(c)

7. Multimedia Network Security Lab. On STUT Proposed scheme I. Color Palette Setup Phase II. Priority Weighting Distribution Phase III. Color Palette Embedding Phase IV. Extracting Secret Data Phase 7

8. Multimedia Network Security Lab. On STUT I. Color Palette Setup Phase (LBG[8] method) Input: Cover-image Output: Color palette with n colors (1). Generate a palette from the cover-image. where x(n) is the non-repeat color pixel in cover-image, and we randomly select y(i), i= from x(n),. (2). Group training by using where d(p,q) is the Euclidean distance between pixel p and pixel q and h ≠ i. (3). If the palette is different from previous palette, then go to (4). Otherwise, stop the phase. (4). Update the initial pixel by where G(i) is the i th group number. Go to (2). 8

9. Multimedia Network Security Lab. On STUT II. Priority Weighting Distribution Phase Input: 256 color palette A and 512 color palette B. Output: High Priority and Low Priority palettes. (1). Compute the Euclidean distance between palette A and B for every pixel. the high priority palette is found out the 256 shortest Euclidean distances from palette B. (2). After comparing with palette A, the high priority palette is found out the 256 shortest Euclidean distances from palette B. (3). The other 256 pixels in palette B are distributed to low priority palette. 9

10. Multimedia Network Security Lab. On STUT III. Color Palette Embedding Phase Input: Cover image and color palette. Output: Stego-image. (1). Compute the difference value between the neighbor pixels. (2). Statistical the difference number by using the histogram. (3). Find out the peak-point and zero-point. (4). Translate the pixel value in color palette into binary data. (5). Using the NSAS method to embed these secret data. 10

11. Multimedia Network Security Lab. On STUT IV. Extracting Secret Data Phase Input: Stego-image. Output: The original image. (1). Compute the difference value between the neighbor pixels. (2). Find the histogram of the stego-image. (3). Scan the histogram from left to right and compute the 4 continuous difference values by using the following equations where y i, y i+1, y i+2, and y i+3 are the numbers of 4 continuous difference value from the ith value. and represent the boundary values, their values are depended on the ratio of the number of bit 0 and bit 1 in the input secret data. (4). If the result such that the boundary condition and then we can find out the secret data is embedded between the middle of 4 continuous differences value. Otherwise, repeat (3) until the whole histogram is scanned completly. 11

12. 12 Cover image Index image Palette with 512 colors High Priority Palette A with 256 colors Low Priority Palette B with 256 colors Embedding procedure By LBG method

13. 13 High Priority Palette A with 256 colors Low Priority Palette B with 256 colors Index image Embedded adaptively in the histogram of different value of index image. [ similar to NSAS method] Stego index image with high quality Stego index image with low quality Embedding procedure Embedded adaptively in the histogram of different value of index image. [ similar to NSAS method] different value # # # 0 1 1

14. 14 Extracting procedure Stego index image with high quality # different value High Priority Palette A with 256 colors Index image Recovered cover image with high quality y 1 y 2 y 3 y 4

15. 15 Experimental result Figure 6(a) Recovered cover image with high quality Figure 6(b) Recovered cover image with low quality Table 1. Recovered image ’ s quality with different priority for 512 color palette.

16. Multimedia Network Security Lab. On STUT Q & A Thanks for listening 16