1 Security and Robustness Enhancement for Image Data Hiding Authors: Ning Liu, Palak Amin, and K. P. Subbalakshmi, Senior Member, IEEE IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 9, NO. 3, APRIL 2007 Receiver 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

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 (MSE A ), 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 betweenand 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.