Robust Coverless Image Steganography Based on DCT and LDA Topic Classification

Abstract

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 (LDA) topic classification is proposed. First, latent dirichlet allocation topic model is utilized for classifying the image database. Second, the images belonging to one topic are selected, and 8 × 8 block discrete cosine 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 steganalysis 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.