Unconstraint Optimal Selection of Side Information for Histogram Shifting Based Reversible Data Hiding Source:  IEEE Access. March, 2019. doi: 10.1109/ACCESS.2019.2903079 Authors: J. Wang, X. Chen and Y. Shi Speaker: 李琳 Date: 2019.03.14

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

Related Works - HS (Ni et al. Method) 114 112 113 115 114 112 113 116 1 114 115 112 113 116 01001000 1 [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.354-362, 2006. Peak bin Zero bin

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’’

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

Performance Evaluation for Conventional HS Based Embedding Process

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

Case 1: When the given payload is 4500 bits

Case 1: When the given payload is 4500 bits

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

IV. RAPID PERFORMANCE EVALUATION OF MULTILEVEL EMBEDDING

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

V. AN EVOLUTIONARY ALGORITHM BASED EFFECTIVE SEARCH METHOD

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)

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

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

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.3524-3533, 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.187-193, 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. 989-999, 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. 368-383, 2012. [28] H. T. Wu and J. Huang, “Reversible image watermarking on prediction errors by efficient histogram modification”, SignalProcess., vol.92,no.12, pp. 3000-3009, 2012.

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

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

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