1. Unconstraint Optimal Selection of Side Information for Histogram Shifting Based Reversible Data Hiding
Source:  IEEE Access. March, doi: /ACCESS
Authors: J. Wang, X. Chen and Y. Shi
Speaker: 李琳
Date:

2. Outline Related Work Proposed Scheme Experiment Result Conclusions
Histogram shifting (HS)
Prediction-error Expansion (PEE)
Proposed Scheme
Experiment Result
Conclusions

3. Related Works - HS (Ni et al. Method)
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[17] Z. Ni, Y. Q. Shi, N. Ansari and W. Su, “Reversible data hiding," IEEE Trans. Circuits Syst. Video Technol., vol.16, no.3, pp , 2006.
Peak bin
Zero bin

5. Predict error after embedding
Related Work PEE -2 1 6 -3 7
Predict error e
Cover Image I
Predict Image I’ p1
embedding -3 2 1 7 -4 8 z1
Predict error after embedding
Predict-error Histogram
Stego Image I’’

6. Proposed Scheme:
The aim of proposed scheme is to determine the unconstraint optimal side information.

7. Performance Evaluation for Conventional HS Based Embedding Process

8. A general optimization model to determine the optimal peak and zero bins as side information associated with minimum distortion:

10. Case 1: When the given payload is 4500 bits

11. Case 1: When the given payload is 4500 bits

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13. Based on both examples, the superiority of
"multilevel embedding" is demonstrated.

14. IV. RAPID PERFORMANCE EVALUATION OF MULTILEVEL EMBEDDING

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17. The flowchart of rapid performance evaluation
Calculate the accelerate shifting value
Evaluate distortion D
Add all those frequencies to acquire rate EC

18. V. AN EVOLUTIONARY ALGORITHM BASED EFFECTIVE SEARCH METHOD

19. To achieve an expected evolution:
Way 1: selection, crossover and mutation(for a rapid search speed)
Way 2: empirical modification(for small search step and controllable search direction)

20. Mutation: another unused one replace current one
Crossover
Selection: Roulette wheel selection
(those one with less distortion to reproduce
the next generation.)

21. EMBEDDING and EXTRACTING PROCESS
Reservation for auxiliary information Aux(O)
Cross set recovery, the rest hidden message
stego-image, λ%, Aux(O),
in the inverse order(Round set ),
extract half of the secret message
Rhombus prediction of Cross set
extract the auxiliary information Aux(O)
EXTRACTION
Stego-pixels generation in the Cross set
Stego-pixels generation in the Round set
Optimal peak and zero bins selection
Cross set embedding

22. Experiment Results
[12] X. L. Li, B. Yang and T. Y. Zeng, “Efficient reversible watermarking based on adaptive prediction-error expansion and pixel selection," IEEE Trans.Image Process., vol.20, no.12, pp , 2011.
[22] L. Luo, Z. Chen, M. Chen, X. Zeng and Z. Xiong, “Reversible image watermarking using interpolation technique," IEEE Trans. Inf. Forensics Security, vol.5, no.1, pp , 2010.
[30] J. Wang, J. Ni, X. Zhang and Y. Shi, “Rate and distortion optimization for reversible data hiding using multiple histogram shifting," IEEE Trans. On Cybernetics, vol. 99, no. 1, pp. 1-12, 2017.
[19] V. Sachnev, H. J. Kim, J. Nam, S. Suresh and Y. Q. Shi, “Reversible watermarking algorithm using sorting and prediction," IEEE Trans.Circuits Syst. Video Technol., vol.19, no.7, pp , 2009.
[27] G. Xuan, X. Tong, J. Teng, X. Zhang and Y. Q. Shi, “Optimal histogram pair and prediction-error based image reversible data hiding," Proc. Int. Workshop Digit.-Forensics Watermarking, Shanghai, China, pp , 2012.
[28] H. T. Wu and J. Huang, “Reversible image watermarking on prediction errors by efficient histogram modification”, SignalProcess., vol.92,no.12, pp , 2012.

27. Conclusions 1. multilevel embedding based
2. expected performance at different payloads.
3. the computation complexity is reduced.
4. stable search
5. an affordable time cost

28. The major weakness of this paper:
Bpp is too low, not to mention too much extra information to hide
2. High time complexity
3. GA may not find out a global optimal solution

29. Thank you for listening!