Chair Professor Chin-Chen Chang Feng Chia University Jan. 2008

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Presentation transcript:

Chair Professor Chin-Chen Chang Feng Chia University Jan. 2008 Using Nearest Covering Codes to Embed Secret Information in Grayscale Images Chair Professor Chin-Chen Chang Feng Chia University Jan. 2008

Data Hiding Hiding system Stego image Cover image 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 Secret message

Cover Carriers Image Video Sound Text

VQ Encoding Index table Original Image Codebook … (120,155,…,80) 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 (90,135,…,120) (100,125,…,150) … Index table Original Image (49,117,…,25) (50,42,…,98) (20,65,…,110) Codebook

Previous Work of Steganography on VQ To find the closest pairs

d(CW0, CW8) > TH d(CW13, CW14) > TH Unused CW0, CW8, CW13, CW14

Encode Index Table CW0, CW8, CW13, CW14 Unused Index Table Original Image Index Table Unused CW0, CW8, CW13, CW14

A secret message: 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 Index Table Secret bits CW1, CW2, CW4, CW5 CW6, CW7 CW11, CW3 CW15, CW10 CW12, CW9 1

A secret message: 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 Index Table Secret bits CW1, CW2, CW4, CW5 CW6, CW7 CW11, CW3 CW15, CW10 CW12, CW9 1

A secret message: 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 Index Table Secret bits

(7, 4) Hamming Coding Encoding Data: 1 1 0 12 d1 d2 d3 d4 Data: 1 1 0 12 Rule: d1  d2  d4  p1 = 0 1  1  1  p1 = 0  p1 = 1 1 0 1 0 1 0 1 Rule: d1  d3  d4  p2 = 0 1  0  1  p2 = 0  p2 = 0 p1 p2 d1 p3 d2 d3 d4 Encoded data Rule: d2  d3  d4  p3 = 0 1  0  1  p3 = 0  p3 = 0

(7, 4) Hamming Coding (Cont.) Corrected data: 1 0 1 0 1 0 1 Error Detection p1 p2 d1 p3 d2 d3 d4 Correcting Parity check matrix H: Received data: 1 0 1 0 1 1 1 b1 b2 b3 b4 b5 b6 b7 Error bit

Inspiration from Hamming Coding (0000000)T = (000)T  010 (0000001)T = (111)T  710 SECRET (0000010)T = (110)T  610 (0000011)T = (001)T  110 : : : (1011100)T = (011)T  310 H : (1111101)T = (110)T  610 (1111110)T = (111)T  710 (1111111)T = (000)T  010

The Proposed Method Pre-process (0000000) : (1111111) C0 (0000011) (0000001)T = 710 C1 : : (0000010)T = 610 (1011100) C3 (0000011)T = 110 : : : : : (1011100)T = 310 (0000010) H : : C6 (1111101) (1111101)T = 610 : (1111110)T = 710 (0000001) (1111111)T = 010 : C7 (1111110)

The Proposed Method (Cont.) Embedding 7 10 9 6 3 4 12 8 11 00000111 00001010 00001001 00000110 00000011 00000100 00001100 00001000 00001011 I (0101100) Finding a nearest covering code (1010100) (1011100) : C3 (1110110) Compute Hamming distance: (1010100)  (0101100) = 4 (1010100)  (1011100) = 1 Minimum (1010100)  (1110110) = 2 :

The Proposed Method (Cont.) Embedding 00000111 00001010 00001001 00000110 00000011 00000100 00001100 00001000 00001011 00000111 00001010 00001001 00000011 00000100 00001100 00001000 00001011 Replace (1010100)  (1011100) = 1 7 10 9 3 4 12 8 11 I’

The Proposed Method (Cont.) Extracting 7 10 9 3 4 12 8 11 00000111 00001010 00001001 00000011 00000100 00001100 00001000 00001011 I’ 0112 H Secret data

Experimental Results Each stego image (512512) carried 112,347 secret bits (i.e. 0.42857 bpp)

Experimental Results (Cont.)

Experimental Results (Cont.)

Conclusions The proposed method has a lower cost of chip implementation and saves the power consumption for mobile devices.