Reversible Data Hiding with Improved Histogram Alteration Method
Reversible data hiding for high quality images using modification of prediction errors
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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
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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 - PowerPoint PPT Presentation
Transcript of Reversible data hiding for high quality images using modification of prediction errors
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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 ShiuPresenter: Chia-Chun WuDate: September 4, 2009
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OUTLINE
• INTRODUCTION
• RELATED WORKS
• PROPOSED SCHEME
• EXPERIMENTAL RESULTS
• CONCLUSIONS
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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。
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INTRODUCTION (1/3)
Reversible data hiding(Lossless Data Hiding)
Modification of Prediction Errors (MPE)
Cover Image
Secret Data
LosslessEmbedding Stego-image
Stego-image LosslessExaction
LosslessCover Image
Secret Data
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INTRODUCTION (2/3)
• Reversible data hiding (Lossless Data Hiding)
• Application:– medical images, military photos, law
enforcement
• Challenges:– Capacity– Quality
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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
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RELATED WORKS (1/3)
5 6 5 6 7
5 6 6 6 5
2 3 5 6 2
1 3 1 0 2
1 2 3 3 2
Histogram ofpixel values
Peak pointZero point
4 6 4 5 7
4 6 6 5 4
2 3 4 5 2
1 3 1 0 2
1 2 3 3 2
Original image
Stego image
Secret data embedding
101100unchanged
5 6 5 6 7
5 6 6 6 5
2 3 5 6 2
1 3 1 0 2
1 2 3 3 2
101100
Ni et al.’s method
Extracting
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min( , ) if max ,
max , if min ,
otherwise,i
a b c a b
p a b c a b
a b c
204 205
203 202
Thodi and Rodriguez’s method
Predicted value xi’ = 2 × pi / 2
Prediction error ei between xi and xi’ ei = xi – xi’Expanded prediction error Ei = 2 × ei + sj
a = 203, b = 205, c = 204, xi = 202
xi’ = 2 × 204 / 2 = 204
ei = xi – xi’= -2
If secret bit sj = 1, Ei = 2 × ei + sj = -3Stego-pixel
yi = xi’ + Ei. ( or yi = xi + ei + sj) yi = 204 + (-3) =201
pi = 204
RELATED WORKS (2/3)
c b
a xi
Embedding phase
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204 205
203 201
Thodi and Rodriguez’s method
Secret bit sj = LSB(yi),
yia
bc
yia
bc
sj = LSB(yi) = LSB(201) = 1Predicted value yi’ = 2 × pi’ / 2
Expanded prediction error Ei = yi – yi’
Prediction error ei = Ei / 2
xi = yi’ + ei (or xi = yi – ei – sj).
min( , ) if max ,
max , if min ,
otherwise,i
a b c a b
p a b c a b
a b c
yi’ = 2 × 204 / 2 = 204
pi’ = 204
Ei = 201 – 204 = -3
ei = -3/ 2 = -2
xi = 204 + (-2) = 202
RELATED WORKS (3/3)
Extracting phase
a = 203, b = 205, c = 204, yi = 201
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PROPOSE SCHEME (1/6)
More suitable
Histograms of prediction errors and histogram of pixels in the spatial domain for images Lena and Baboon.
11Embedding phase
PROPOSE SCHEME (2/6)
min( , ) if max ,
max , if min ,
otherwise,i
a b c a b
p a b c a b
a b c
Prediction error ei = xi – pi.
c ba x
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154 156 153 153
154
154
151
'1,1 1 157I p e
'1,2 1 149I p e
154 156 153 153
154 156 150 148
154 157 157 157
151 158 157 155
e1 = x1 – p1 = 0 : embeddable e = e + 1 = 1
c ≤ min (a, b) → p1 = 156
Secret = 1012
Original image IStego image I’
154 156
154
e2 = x2 – p2 = -4 : non-embeddable e2 = e2 – 1 = -5
p2 = 154
p5 = 150e5 = x5 – p5 = 7, all secret bits are embedded, set L=(2,2)
'2,2 5 157I p e
157 149
153156
157 148
158 157 157
158 157 155
PROPOSE SCHEME (3/6)
stoppinglocation L
13Extracting phase
PROPOSE SCHEME (4/6)
min( , ) if max ,
max , if min ,
otherwise,i
a b c a b
p a b c a b
a b c
Prediction error ei = xi – pi.
c ba x
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154 156 153 153
154
154
151
154 156 153 153
154 157 149 148
154 158 157 157
151 158 157 155
Original image IStego image I’
'' '1,1 1 156I p e
e1 = x1’ – p1’ = 1: secret bit = 1e = e - 1 = 0
c ≤ min (a, b) → p1’ = 156
156
'' '1,2 2 154 ( 4) 150I p e
e2 = x2’ – p2’ = -5: no secret bite = e + 1 = -4
p2’ = 154
154 156
154 150
156 153
157
153 153
149
'' '1,3 3 149 ( 1) 148I p e
e3 = x3’ – p3’ = -1: secret bit = 0
p3’ = 149
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PROPOSE SCHEME (5/6)
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154 156 153 153
154
154
151
154 156 153 153
154 157 149 148
154 158 157 157
151 158 157 155
Original image IStego image I’
'' '1,1 4 157I p e
e1 = x1’ – p4’ = 1: secret bit = 1e = e - 1 = 0
c ≤ min (a, b) → p4’ = 157
156 150 148154 157
154 157
157 149
158
'' '2,2 5 157I p e
e1 = x1’ – p5’ = 7L =(2,2): all embedded message has been extracted
p5’ = 150
157 157
158 157 155
PROPOSE SCHEME (6/6)
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EXPERIMENTAL RESULTS (1/6)
Experimental results of some commonly used images
pure payload of MPE - pure payload of Ni et al. methodIncreased payload
pure payload of Ni et al. method
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EXPERIMENTAL RESULTS (2/6)
Comparison of PSNR with same embedding capacity
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EXPERIMENTAL RESULTS (3/6)
Experimental results for 23 natural photographic test
images sized 768 × 512 (payload is measured in bits).
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EXPERIMENTAL RESULTS (4/6)
Experimental results for test images
20Capacity versus distortion performance of various methods for test images
EXPERIMENTAL RESULTS (5/6)
21Capacity versus distortion performance of various methods for test images
EXPERIMENTAL RESULTS (6/6)
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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.
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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。
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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 ) 。
