1

Security and Robustness Enhancement

for Image Data Hiding

Authors: Ning Liu, Palak Amin, and K. P. Subbalakshmi, Senior M

ember, IEEE

IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 9, NO. 3, APRIL 2007Receiver October 6, 2005;revised August 1, 2006

Adviser: Chih-Hung Lin

Speaker: Shau-Shiang Hung

Date : 98/10/13

2

Outline

• Author• Introduction• Related Work: LUT Embedding• Proposed Hash-Based Randomized Embedding • Optimizing HRE Against JPEG Attack• Experimental Result• Conclusion

3

Authors

4

Author(1)

In 1995,recived the B.E in electrical engineering from the Sichuan University, China.

In 2001,recived the M.E in signal processing engineering form the Tongji University, China.

Since 2002, pursuing the Ph.D. degree in Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ.

Now, Working in the MSyNC Lab under the guidance of Prof. K. P. Subbalakshmi.

Ning Liu

5

Author(2)

Palak Amin

In 2003, received the B.E. and the M.E. in computer engineering form Stevens Institute of Technology, Hoboken, NJ.

Now he is currently pursuing the Ph.D.

6

Author(3)

K.P. Subbalakshmi

An Assistant Professor in Electrical and Computer Engineering, Steven Institute of Technology, Hoboken, NJ.

Research interests are in the areas of information and communication security, joint source channel coding.

7

Introduction

8

I. Introduction(1)

• DIGITAL data hiding application

• Optimal Data hiding

9

I. Introduction(2)

• A fundamental issue, “Tradeoff” • Capacity ,robustness, and distortion.

• Another equally important parameter• Security of the hide data.

10

I. Introduction(3)

• In 1998, a lattice based embedding algorithm

» (J. Chae, D. MukherJee, and B. Manjunath, “Color image embeddingusing multidimensional lattice structures,” in Proc. IEEE Int. Conf.Image Processing (ICIP’98), 1998.)

• In 2003, Using a look up table (LUT)» M. Wu, “Joint security and robustness enhancement

for quantization based data embedding,” IEEE Trans. Circuits Syst. Video Technol., vol.13, pp. 831–841, Aug. 2003.

11

I. Introduction(4)

• Propose an algorithm– Hash-based randomized embedding algorithm

(HRE)

12

II. Related Work: LUT Embedding

13

II. Related Work: LUT Embedding(1)

14

• For example, q=10, and LUT:

• Embed a “1” in a pixel value “78”

II. Related Work: LUT Embedding(2)

15

II. Related Work: LUT Embedding(3)

• Although the closest multiple of 10 to 78 is 80,T(80)=0,the algorithm picks “60” as the watermarked pixel value.

•

if

Then,be embed

16

II. Related Work: LUT Embedding(4)

• The entropy rate

• The mean squared error of LUT (MSEA), run r

17

III. Proposed Hash-Based Randomized Embedding Algorithm

18

III. Proposed Hash-Based Randomized Embedding Algorithm(1)

• Encoder Framework

19

III. Proposed Hash-Based Randomized Embedding Algorithm(1.1)

• Partition the Host Sequence into Subsequences:– M=R/L, M = even

20

III. Proposed Hash-Based Randomized Embedding Algorithm(1.2)

21

III. Proposed Hash-Based Randomized Embedding Algorithm(1.3)

• Parameter Calculation:– If use SCS scheme

– Else, if compensating for distortion due to JPGE attack

OR

22

III. Proposed Hash-Based Randomized Embedding Algorithm(1.4)

• Embedding Process:– Parameter t.

– For each

– Embed one bit by using the SCS scheme

23

III. Proposed Hash-Based Randomized Embedding Algorithm(2)

• Decoder Framework

• Security Analysis– Attacker

1) Exhaustive search in the entire key space.

2) Statistical analysis from the probability distribution of the stego-sequence

24

III. Proposed Hash-Based Randomized Embedding Algorithm(3)

25

III. Proposed Hash-Based Randomized Embedding Algorithm(2.1)

• Attack 2)

– Let– Entropy of X with hash function

=

With the 2L components in this table

26

III. Proposed Hash-Based Randomized Embedding Algorithm(2.2)

27

IV. Optimal HRE Against JPEG Attack

28

IV. Optimal HRE Against JPEG Attack(1)

JPEG algorithm uses quantizers organized in a table, called the quantization table Q

29

IV. Optimal HRE Against JPEG Attack(2)

A. Optimal Embedding for the JPEG Compression Attack.

B. Optimization of

30

IV. Optimal HRE Against JPEG Attack(3.1)

A. Optimal Embedding for the JPEG Compression Attack – From quantization theory

• The uniform quantizer can be modeled as an additive uniform noise channel with variance

31

IV. Optimal HRE Against JPEG Attack(3.2)

• HRE is also uniform quantizer based

• Uniform nosie as

• Improve capacity in lower WNR range

• Assumption

32

IV. Optimal HRE Against JPEG Attack(3.2.1)

33

IV. Optimal HRE Against JPEG Attack(3.2.2)

34

IV. Optimal HRE Against JPEG Attack(3.2.3)

35

IV. Optimal HRE Against JPEG Attack(3.3)

• Uniform quantizer based embedding with DC– Two parameter:

and

36

IV. Optimal HRE Against JPEG Attack(3.3.1)

37

IV. Optimal HRE Against JPEG Attack(3.3.2)

38

IV. Optimal HRE Against JPEG Attack(3.4)

Depend on relation between and

So there are two cases of

39

IV. Optimal HRE Against JPEG Attack(3.4)

B. Optimization of Case 1:

And

40

IV. Optimal HRE Against JPEG Attack(3.4.1)

41

IV. Optimal HRE Against JPEG Attack(3.5)

• B. Optimization of Case 2:

42

IV. Optimal HRE Against JPEG Attack(3.5.1)

43

V. Experimental Result

44

V. Experimental Result(1)

45

V. Experimental Result(2)

46

V. Experimental Result(3)

47

V. Experimental Result(4)

48

V. Experimental Result(5)

49

Conclusion

50

Conclusion(1)

• This HRE algorithm can be increased independent of capacity, robustness and embedding induced distortion

• The proposed HRE algorithm provides joint security and robustness improvement over the traditional quantization based embedding scheme and LUT algorithm.

51

Conclusion(2)

• Experimental results showed that the proposed scheme achieves the same embedding capacity as SCS against AWGN attack

• On the other hand, the algorithm causes 7 dB less distortion than STDM at fixed embedding rate and robustness against JPEG compression attack.