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 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.

Currently, wide attention has been paid to reversible data hiding (RDH) in encrypted domain due to the popular de-ployment of cloud storage. However, nearly all existing RDH schemes in encrypted domain are proposed for raster images, and very little work has been done to 2D vector graphics which is rep-resented in real numbers. In this paper, a reversible mapping model for real numbers is first built. It maps the points in R nto 2snon-intersecting subsets in R n, which guarantees that s bits can be embedded into each real number. Based on the model, an RDH scheme in encrypted 2D vector graphics is put forward. In the scheme, a user encrypts a 2D engineering graphics and store it in the cloud, and then the cloud service provider (CSP) can perform information hiding, extraction, and even recover the encrypted 2D vector graphics. For the authorized user, it can acquire the recov-ered 2D vector graphics from the cloud and obtain its original ver-sion after decryption. While for an unauthorized user, he can only acquire the encrypted 2D vector graphics with hidden message, and only an approximate 2D vector graphics can be obtained even he knows the decryption key but does not know the hiding key. Experimental results and analysis show that it can strike a good balance between security, distortion and capacity. It provides a new paradigm for RDH in encrypted domain for the data repre-sented in real numbers.

Reversible data hiding in encrypted domain (RDH-ED) can perform data encryption to fulfill the privacy protection of original media and embed additional data for covert communication or access control. However, current researches are focusing on the encrypted images, and little attention is paid to encrypted three-dimensional (3D) models. In this paper, a high capacity separable RDH-ED method for encrypted 3D models is proposed based on octree spatial subdivision and multiple most significant bit (multi-MSB) prediction. Firstly, a 3D model is adaptively subdivided into non-overlapping subblocks by octree spatial subdivision, and the vertices in a subblock are classified into embedding set and reference set. To better utilize the spatial correlation of the two sets, the multi-MSB prediction error of the embedding set is used to embed the additional data, and the reference set is used to losslessly recover the embedded set. Then, the model is encrypted by a specified encrypted algorithm. At last, additional data is embedded into the reserved embedding room by multi-MSB substitution. Experimental results show that the proposed method can achieve a higher embedding capacity compared with the state-of-the-art methods, and guarantee the lossless recovery of the 3D model.

To further enhance the nonlinear and cryptographic properties of chaotic S-boxes, a novel S-box design is proposed based on the discrete chaotic map and improved artificial bee colony algorithm. Firstly, the S-box generated by chaotic sequence is initialized by the opposition-based optimization. Then, the dual transposition artificial bee colony algorithm is used to optimize the population, where the Gaussian swap is engaged to strengthen the optimum efficiency, and make the population acquire a better quality with each other. With the Gaussian mutation operator, the opposition-based optimization performs self-learning to obtain better population. Comparing with some recent S-boxes based on chaos. The S-boxes generated by the proposed scheme can achieve better performance in bijection, nonlinearity, strict avalanche criterion(SAC), bit independence criterion(BIC), I / O XOR distribution and linear approximation probability(LP). It provides a new and effective method for designing S-boxes with good cryptographic properties.

Aiming to reduce the distortion in the existing reversible watermarking for 3D mesh models, this paper generalizes two-dimensional (2D) region nesting to ${n}$ -dimensional space, and proposes a general region nesting based semi-fragile reversible watermarking for authenticating 3D mesh models by combing watermark generation based on vertex projection and mesh topology. For any ${n}$ -dimensional space, each hypercube space is partitioned into several nested watermark sub-spaces, and the watermark is embedded by placing the original vertex and the mapped vertex on a straight line. The watermark is extracted by acquiring the position of the vertices, and its original coordinates can be restored by inverse mapping. Furthermore, a new coordinate system is built for 3D mesh models based on reference vertices to achieve semi-fragility, and the watermarks generated by vertex projection and mesh topology is utilized for integrity authentication. Experimental results and analysis show that the proposed scheme can significantly reduce the distortion, achieve semi-fragility in RST (Rotation, Scaling and Translation) and vertex reordering, identify the tampering type, and accomplish tampering location at vertex level. It has great potential to be applied for content authentication of 3D mesh models.

Aiming at reducing the large distortion of the existing reversible watermarking for three-dimensional(3D) mesh models and meeting the needs of authentication in cloud-based data storage, a semi-fragile reversible watermarking for authenticating 3D models in dual domains is proposed based on variable direction double modulation. A 3D model is first transformed into the spherical coordinate system, and then the direction of quantization modulation is changed under specific circumstances to achieve less distortion. Watermarks are determined by the relative position and vertices number in the one-ring neighbourhood of all vertices, so the watermark generation is independent of the coordinate system and robust to RST(rotation, scaling, and translation). In addition, the watermarks are generated and embedded in both plaintext domain and encrypted domain, and the authentication can be accomplished in both domains. Experimental results and analysis show that the proposed scheme can effectively detect malicious tampering in two domains and reduce the distortion caused by watermark embedding. It has potential application in the content authentication of 3D models in cloud-based data storage.