Aiming at the problem that the embedding rate and robustness of coverless information hiding cannot be well balanced, a robust coverless information hiding scheme based on texture feature classification and synthesis is proposed. In this scheme, texture image features are extracted with spatial pyramid algorithm, and classification models are obtained by supervised classification training. A mapping dictionary is constructed according to the classification of image blocks and different location information. The sender chooses image blocks based on secret information and combines all image blocks into one image according to public key, then generates complex lines through reversible deformation. The texture image can be restored to image blocks by using the key, and the classification model is used to identify the classification of image blocks and determine the location information. Finally, secret information is extracted based on the mapping dictionary. Experimental results show that the proposed scheme has strong robustness against JPEG compression, Gaussian noise, salt and pepper noise and other typical attacks, and the embedding capacity can be further improved with the increase of image category number.
SI Guangwen, QIN Chuan, YAO Heng, HAN Yanfang, ZHANG Zhichao
. Robust Coverless Data Hiding Based on Texture Classification and Synthesis[J]. Journal of Applied Sciences, 2020
, 38(3)
: 441
-454
.
DOI: 10.3969/j.issn.0255-8297.2020.03.010
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