混合蛙跳优化决策面的LSB±k隐写算法

doi:10.3969/j.issn.0255-8297.2015.06.010

Abstract

Abstract: Most improved steganographies based on intelligent optimization cannot realize high-dimensional features optimization simultaneously. To solve the problem, this paper proposes an improved LSB±k steganography (denoted SFLA-LSB±k) based on SFLA to achieve an optimal decision surface. Different from the other improved steganographies that attempt to keep image features unchanged after data embedding as much as possible, the proposed method tries to randomly change feature directions of stego-image in the embedding process optimized by SFLA. Thus, it is difficult to find a decision surface to distinguish cover images from stego-images. Simulation indicates that, with the same embedding capacity, SFLA-LSB±k demonstrates better performance in resisting steganalysis than the traditional LSB±k and the improved LSB±k optimized by PSO. Especially, the AUC value is reduced to 0.5637 when k=1 against steganalysis with 78-dimension features.

Key words: LSB±, ksteganography, SFLA, decision surface, steganalysis

CLC Number:

TP911.7

OUYANG Chun-juan, LIU Chang-xin, LIU Huan. Improved LSB±k Steganography Based on Decision Surface Optimized by SFLA[J]. Journal of Applied Sciences, 2015, 33(6): 663-670.

References

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Improved LSB±k Steganography Based on Decision Surface Optimized by SFLA

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