1. Improvement of Multi-bit Information Embedding Algorithm for Palette-Based Images Anu Aryal, Kazuma Motegi, Shoko Imaizumi and Naokazu Aoki Division of Advanced Integration Science Chiba University, Japan 1 ISC 2015, 11 th September, 2015

2. Outline Background Current Research Results Conclusion 2

3. Outline Background Current Research Results Conclusion 3

4. Introduction  Steganography [1] is the art and science of hiding information by embedding it in some other data.  Unauthorized recipients unaware about the existence of embedded data. 4 [1] Kahn, D.: The history of steganography. In: Goos, G., Hartmanis, J. (eds.) The First International Workshop on Information Hiding. LNCS, vol.1174, pp.1-5, Springer, Heidelberg (1996).

5. Conventional method [2]  Embeds each k-bits message into pixels of 2 × 2 pixel matrix as shown in Fig. 1.  Based on embedding a message into the pixels by assigning a parity to each pixel matrix according to the Euclidean distance. Fig. 1. Embedded unit of Conventional Method [2]. 5 [2] Imaizumi, S., Ozawa, K.: Palette-Based Image Steganography for High-Capacity Embedding. Bull. Soc. Photogr. Imag. Japan, Vol.25, No. 1, pp.7-11 (2015).

6. Conventional method [2] contd. Drawbacks  At maximum, only 3/4 bit per pixel can be embedded.  Maximum embedded amount is smaller than those methods that embed one bit message into one pixel [3-6].  Tendency to occur large color difference that leads to image degradation. 6 [2] Imaizumi, S., Ozawa, K.: Palette-Based Image Steganography for High-Capacity Embedding. Bull. Soc. Photogr. Imag. Japan, Vol.25, No. 1, pp.7-11 (2015). [3] Tzeng, C.-H., Yang, Z.-F., Tsai, W.-H.: Adaptive data hiding in palette images by color ordering and mapping with security protection. IEEE Trans. Commun. 52(5), 791-800 (2004). [4] Fridrich, J.: A new steganographic method for palette-based image. In: Proc. Of IS&T PICS, pp.285-289 (1999). [5] Huy, P.T., Thanh, N.H., Thang, T.M., Dat, N.T.: On fastest optimal parity assignments in palette images. In: Intelligent Information and Database Systems,vol. 7197, pp. 234-244 (2012). [6] Inoue, K., Hotta, S., Takeichi, Y., Urahama, K.: A Steganographic Method for Palette-Based Images [in Japanese]. In: The Transactions of the Institute of Electronics, Information and Communication Engineers. A, vol. 82, no.11, pp.1750-1751 (1999)

7. Motivation  High capacity of embedding.  Suppression of degradation of image quality.  Conventional method [2] has space to improve both. 7 [2] Imaizumi, S., Ozawa, K.: Palette-Based Image Steganography for High-Capacity Embedding. Bull. Soc. Photogr. Imag. Japan, Vol.25, No. 1, pp.7-11 (2015).

8. Outline Background Current Research Results Conclusion 8

9. Current Research Concept  Embed each k bit message into 1×3 pixel matrix as shown in Fig. 2.  Embed message into a limited color by controlling the index values. Fig. 2. Embedded unit of proposed method (k = 3). 9

10. Proposed Method (1) 1)Sorting the color palette. 1)Embedding of message. 10

11. Proposed Method (2) 1.Sorting color palette using CIEDE2000 [7]. 11  Find the darkest color in entries C i.  Set the increment j = j +1.  Calculate ∆ E 00 between initial entries C i and entries C’ j-1.  Indices are assigned to all the entries. [7] Colorimetry - Part 6: CIEDE2000 Colour-difference formula. ISO/CIE 11664-6 (2014).

12. Proposed Method (3) 2. Embedding of message.  Select 1×3 pixel matrix from the target image (message length is k = 3 bits) as shown in Fig. 2. Fig. 2. Embedded unit of proposed method (k = 3). 12

13. Proposed Method (4)  Calculate parity S n as S n = d 0(n) + d l(n) mod 4, where d l(n) indicates the index of pixel t l(n). Fig. 2. Embedded unit of proposed method (k = 3). 13

14. Proposed Method (5)  The value of S n can be controlled by changing the indices of p (n), t 0(n) and t 1(n) by +1 or -1. Fig. 2. Embedded unit of proposed method (k = 3). 14 PnPn SnSn Embedding Information w n 037 026 015 004 103 112 121 130 Table 1. Example of P n, S n and embedded information w n.

15. Proposed Method (6) PnPn SnSn Embedding Information w n 037 026 015 004 103 112 121 130 Table 1. Example of P n, S n and embedded information w n. Index of p (n) = Even Index of p (n) = Odd 15 pnpn t 0(n) t 1(n) Target matrix Value of P n before and after embedding Index of p (n) ∆ E 00 DifferentChange by +1 or -1Smaller Difference between S n before and after embedding Index of t 0(n) and t 1(n) ∆ E 00 2Change by +1 or -1Smaller Difference between S n before and after embedding Index of t 0(n) or t 1(n) ∆ E 00 1Change by +1 or -1Smallest Difference between S n before and after embedding Index of t 0(n) or t 1(n) 0Not changed

16. Proposed Method (7)  Embedded message w n can be extracted after calculating P n and S n.  Performs embedding process only when all ∆ E 00 values for the pixels of matrix become 5.0 or less else not.  Steps are repeated until all the messages are embedded. 16

17. Proposed Method (8) Fig. 3. Color blocks and isolated colors.  Each block has been generated by delimiting colors when ∆E 00 > 5.0. 17

18. Proposed Method (9) Proposed Method Conventional Method [2] Fig. 4. Matrix arrangement for maximum amount of embedded bits. Fig. 5. Matrix arrangement for minimum amount of embedded bits. Proposed Method Conventional Method [2]  Maximum amount of embedded bits.  Minimum amount of embedded bits. 18 [2] Imaizumi, S., Ozawa, K.: Palette-Based Image Steganography for High-Capacity Embedding. Bull. Soc. Photogr. Imag. Japan, Vol.25, No. 1, pp.7-11 (2015).

19. Outline Background Current Research Results Conclusion 19

20. Experimental Setups  Amount of embedded bits: 10,800 and 21,600 bits.  Used images: 256×256 pixels, 8-bit color bitmap images.  Number of images: 12  Image quality metrics: PSNR and SSIM. 20

21. Structural Similarity (SSIM) [8]  SSIM is introduced to measure the quality of distored images.  SSIM has Luminance Comparison l(x,y), Contrast comparison c(x,y) and Structure comparsion s(x, y). Therefore, 21 Fig. 6 Diagram of SSIM measurement system. [8] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. In: IEEE Trans. on Image Processing.13 (4), pp.600-612 (2004)

22. Simulation Result (I) Original (Pepper) 22 Proposed method (10,800 bits) SSIM = 0.871 PSNR = 40.34 Conventional method (10,800 bits) SSIM = 0.831 PSNR = 36.58

23. Simulation Result (I) Original (Pepper) 23 Proposed method (21,600 bits) SSIM = 0.754 PSNR = 37.19 Conventional method (21,600 bits) SSIM = 0.699 PSNR = 33.67

24. Simulation Result (III) Original (Balloon) 24 Proposed method (10,800 bits) SSIM = 0.867 PSNR = 43.49 Conventional method (10,800 bits) SSIM = 0.822 PSNR = 40.73

25. Simulation Result (IV) Original (Balloon) 25 Proposed method (21,600 bits) SSIM = 0.751 PSNR = 40.62 Conventional method (21,600 bits) SSIM = 0.674 PSNR = 37.68

26. Quantitative Evaluation (I) 10,800 bits 21,600 bits Fig. 7. Evaluation using PSNR. 26

27. Quantitative Evaluation (II) 10,800 bits 21,600 bits Fig. 8. Evaluation using SSIM. 27

28. Quantitative Evaluation (III) Embedded bitsProposed MethodConventional method [2] Maximum bits65, 28049,152 Minimum bits39,16821,675 Table 2. Maximum and minimum values of embedded bits.. 28 [2] Imaizumi, S., Ozawa, K.: Palette-Based Image Steganography for High-Capacity Embedding. Bull. Soc. Photogr. Imag. Japan, Vol.25, No. 1, pp.7-11 (2015).

29. Outline Background Current Research Results Conclusion 29

30. Conclusion  Enhances data embedding with larger capacity. - Maximum amount of embedded bits is 1.3 times and Minimum amount is 1.8 times more than conventional method.  Improves the image quality by suppressing image quality degradation. 30

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