1. 1 Reversible data hiding for high quality images using modification of prediction errors Source: The Journal of Systems and Software, In Press, Corrected Proof, Available online 3 June 2009 Authors: Wien Hong, Tung-Shou Chen, and Chih-Wei Shiu Presenter: Chia-Chun Wu Date: September 4, 2009

2. 2 OUTLINE INTRODUCTION RELATED WORKS PROPOSED SCHEME EXPERIMENTAL RESULTS CONCLUSIONS

3. 3 要解決的問題 此篇論文主要是利用相鄰像素值非常相近 的特性，以周圍相鄰的像素值來對要進行 隱藏的像素值先進行預測的動作，並計算 預測值跟實際值的差值，接著結合 Ni 等人提 出來的直方圖無失真資料隱藏的方法，藉 由調整預測誤差值來達到達到高容量、低 失真的無失真資料隱藏的目的。

4. 4 INTRODUCTION (1/3) Reversible data hiding (Lossless Data Hiding) Modification of Prediction Errors (MPE) Cover Image Secret Data Lossless Embedding Stego-image Lossless Exaction Lossless Cover Image Secret Data

5. 5 INTRODUCTION (2/3) Reversible data hiding (Lossless Data Hiding) Application: –medical images, military photos, law enforcement Challenges: –Capacity –Quality

6. 6 INTRODUCTION (3/3) Reversible data hiding schemes: –Difference expansion Reversible data embedding using a difference expansion, Jun Tian, IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 8, pp. 890 – 896, Aug. 2003 Reversible watermark using the difference expansion of a generalized integer transform, Alattar, A.M. IEEE Transactions on Image Processing, vol. 13, no. 8, pp. 1147 - 1156, Aug. 2004 Adaptive lossless steganographic scheme with centralized difference expansion, C.C. Lee, H.C. Wu, C.S. Tsai, and Y.P. Chu, Pattern Recognition, vol. 41, no. 6, pp. 2097-2106, 2008 –Histogram modification Reversible data hiding, Z. Ni, Y.Q. Shi, N. Ansari, and W. Su, IEEE Transactions on Circuits and Systems for Video Technology, vol.16, no.3, pp. 354 – 362, March 2006 Hiding Data Reversibly in an Image via Increasing Differences between Two Neighboring Pixels, C.C. Lin and N.L. Hsueh, IEICE Transactions on Information and Systems, vol. E90–D, no.12, Dec. 2007 A lossless data hiding scheme based on three-pixel block differences, C.C. Lin and N.L. Hsueh, Pattern Recognition vol. 41, no. 4, pp. 1415 – 1425, April 2008

7. 7 RELATED WORKS (1/3) 56567 56665 23562 13102 12332 Histogram of pixel values Peak point Zero point 46457 46654 23452 13102 12332 Original image Stego image Secret data embedding 101100 unchanged 56567 56665 23562 13102 12332 101100 Ni et al.’s method Extracting

8. 8 204205 203202 Thodi and Rodriguez’s method Predicted value x i ’ = 2 ×  p i / 2  Prediction error e i between x i and x i ’ e i = x i – x i ’ Expanded prediction error E i = 2 × e i + s j a = 203, b = 205, c = 204, x i = 202 x i ’ = 2 ×  204 / 2  = 204 e i = x i – x i ’= -2 If secret bit s j = 1, E i = 2 × e i + s j = -3 Stego-pixel y i = x i ’ + E i. ( or y i = x i + e i + s j ) y i = 204 + (-3) =201 p i = 204 RELATED WORKS (2/3) cb axixi Embedding phase

9. 9 204205 203201 Thodi and Rodriguez’s method Secret bit s j = LSB(y i ), s j = LSB(y i ) = LSB(201) = 1 Predicted value y i ’ = 2 ×  p i ’ / 2  Expanded prediction error E i = y i – y i ’ Prediction error e i =  E i / 2  x i = y i ’ + e i (or x i = y i – e i – s j ). y i ’ = 2 ×  204 / 2  = 204 p i ’ = 204 E i = 201 – 204 = -3 e i =  -3/ 2  = -2 x i = 204 + (-2) = 202 RELATED WORKS (3/3) Extracting phase a = 203, b = 205, c = 204, y i = 201

10. 10 PROPOSE SCHEME (1/6) More suitable Histograms of prediction errors and histogram of pixels in the spatial domain for images Lena and Baboon.

11. 11 Embedding phase PROPOSE SCHEME (2/6) Prediction error e i = x i – p i. cb ax

12. 12 154156153 154 151 154156153 154156150148 154157 151158157155 e 1 = x 1 – p 1 = 0 : embeddable e = e + 1 = 1 c ≤ min (a, b) → p 1 = 156 Secret = 101 2 Original image I Stego image I’ 154156 154 e 2 = x 2 – p 2 = -4 : non-embeddable e 2 = e 2 – 1 = -5 p 2 = 154 p 5 = 150 e 5 = x 5 – p 5 = 7, all secret bits are embedded, set L=(2,2) 157149 153 156 157 148 158157 158157155 PROPOSE SCHEME (3/6) stopping location L

13. 13 Extracting phase PROPOSE SCHEME (4/6) Prediction error e i = x i – p i. cb ax

14. 14 154156153 154 151 154156153 154157149148 154158157 151158157155 Original image IStego image I’ e 1 = x 1 ’ – p 1 ’ = 1: secret bit = 1 e = e - 1 = 0 c ≤ min (a, b) → p 1 ’ = 156 156 e 2 = x 2 ’ – p 2 ’ = -5: no secret bit e = e + 1 = -4 p 2 ’ = 154 154156 154 150 156153 157 153 149 e 3 = x 3 ’ – p 3 ’ = -1: secret bit = 0 p 3 ’ = 149 148 PROPOSE SCHEME (5/6)

15. 15 154156153 154 151 154156153 154157149148 154158157 151158157155 Original image IStego image I’ e 1 = x 1 ’ – p 4 ’ = 1: secret bit = 1 e = e - 1 = 0 c ≤ min (a, b) → p 4 ’ = 157 156 150 148 154 157 154 157 149 158 e 1 = x 1 ’ – p 5 ’ = 7 L =(2,2): all embedded message has been extracted p 5 ’ = 150 157 158157155 PROPOSE SCHEME (6/6)

16. 16 EXPERIMENTAL RESULTS (1/6) Experimental results of some commonly used images

17. 17 EXPERIMENTAL RESULTS (2/6) Comparison of PSNR with same embedding capacity

18. 18 EXPERIMENTAL RESULTS (3/6) Experimental results for 23 natural photographic test images sized 768 × 512 (payload is measured in bits).

19. 19 EXPERIMENTAL RESULTS (4/6) Experimental results for test images

20. 20 Capacity versus distortion performance of various methods for test images EXPERIMENTAL RESULTS (5/6)

21. 21 Capacity versus distortion performance of various methods for test images EXPERIMENTAL RESULTS (6/6)

22. 22 CONCLUSIONS The embedding capacity of proposed scheme is much higher than that of Ni et al.’s method. The visual quality of the proposed method is better than that of Thodi’s method.

23. 23 此篇論文之優缺點 優點： – 因為一般影像而言，統計完預測誤值的結果後， Peak bin 的 index 都是 0 ，因此，跟 Ni. 等人的方法比起來，此方法不 需額外記錄 Zero bin 及 Peak bin 的資訊。 –Ni. 等人的方法的方法，不論要藏入的資料量多大，整張影 像中每個像素值都會被修改變動到，此方法利用 Stopping Location L 來記錄 Secret Data 最後藏完時的座標位址，在 這座標之後的像素值就完全不做任何修改或變動，來降低 影像失真的程度。 缺點： – 跟 Ni. 等人的方法比起來，此方法要額外記錄及傳送 Stopping Location L 的資訊給接收端。

24. 24 研究方向 本方法是藉由相鄰的 3 個像素值來進行預測的動作， 若額外多考慮相鄰 1 個像素值的情況下或是利用其它 預測的方法，也許可以提高預測的準確度，當預測的 準確度愈高的情況下， Peak bin 就愈集中在 0 的地方， 相對的最大可以隱藏的資料量就會提高 (Peak bin 的 數量決定隱藏量的大小 ) 。