Robust Coverless Image Steganography based on DCT and LDA Topic Classification
In order to improve the robustness and capability of resisting image steganalysis, a novel coverless image steganography algorithm based on Discrete Cosine Transform and Latent Dirichlet Allocation topic classification is proposed. Firstly, LDA topic model is utilized for classifying the image database. Secondly, the images belong to one topic are selected, and 8×8 block DCT transform is performed to these images. Then robust feature sequence is generated through the relation between Direct Current coefficients in the adjacent blocks. Finally, an inverted index which contains the feature sequence, dc, location coordinates and image path is created. For the purpose of achieving image steganography, the secret information is converted into a binary sequence and partitioned into segments, and the image whose feature sequence equals to the secret information segments is chosen as the cover image according to the index. After that, all cover images are sent to the receiver. In the whole process, no modification is done to the original images. Experimental results and analysis show that the proposed algorithm can resist the detection of existing steganalysis algorithms, and has better robustness against common image processing and better ability to resist subjective detection compared with the existing coverless image steganography algorithms. Meanwhile, it is resistant to geometric attacks to some extent. It has great potential application in secure communication of big data environment.
The existing coverless image steganography algorithms can be classified into two categories according to different steganography principles: coverless image steganography based on texture synthesis and coverless image steganography based on mapping rules. I) Coverless Image Steganography Based on Texture Synthesis ii)Coverless Image Steganography Based on Mapping Rules
From the above analysis, the existing coverless image steganography algorithms based on the mapping rules all extract feature sequence in spatial domain, and the robustness is limited. Meanwhile, one image can only hide a fixed length of secret information segment. Furthermore, random selection of cover images may lead to security issues. In order to solve the fore-mentioned problems in the coverless image steganography based on mapping rules, a coverless image steganography algorithm based on transform domain and topic classification is proposed in this paper
A coverless image steganography scheme based on DCT transform is proposed in this paper. The secret information is hidden through the mapping rules, and the cover images are not modified in the steganography process. It can effectively resist the detection of the existing steganalysis algorithms. Meanwhile, LDA topic model is exploited to enhance the capability of resisting detection from subjective vision. Furthermore, the feature sequence is generated based on DC coefficients, which is helpful to improve the robustness, and the search efficiency is optimized by constructing an inverted index. Experimental results and analysis show that the proposed algorithm can achieve good performance in subjective and objective detection resistance on testing database, and it is robust against most image processing attacks, and geometric attacks. The future work will be focused on the improvement of capacity with less side information, along with robustness against rotation and the content loss attacks.
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