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.

Designing selective encryption (SE) schemes for H.265/HEVC has been attracted much attention with the advent of H.265/HEVC codec in the past two decades. However, most SE algorithms for H.265/HEVC encrypt the syntax elements in bypass mode to keep the bit rate. Moreover, the edge region of the video data is not sufficiently protected. To produce large visual distor-tion and edge loss, a tunable SE scheme for H.265/HEVC based on chroma intra prediction mode (IPM) and coefficient scrambling is proposed. Firstly, a pseudo-random number sequence is generated by AES-CTR. Then, the prediction, residual and reconstruction information in H.265/HEVC encoding process are encrypted by a pseudo-random sequence. It encrypts the syntax elements of con-text-based adaptive binary arithmetic coding (CABAC) in bypass mode. Some syntax elements including chroma IPM in regular mode are encrypted as well. To further protect the edge infor-mation, a coefficient scrambling is adopted. The edge information of each frame is extracted and the Transform Units (TUs) are clas-sified according to it. Then, coefficients of the TUs containing edge are scrambled. Finally, a sign used for marking the type of each TU is embedded into a coefficient. Experimental results and anal-ysis show that the proposed scheme has better visual distortion and subjective evaluation results compared with some existing H.265/HEVC SE algorithms. Meanwhile, users can flexibly use the proposed SE scheme according to encryption performance and bit rate requirements, which is attractive in the scenario of protecting video in cloud servers.

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.

To achieve high tampering localization precision and low distortion, a semi-fragile reversible watermarking for authen-ticating 2D engineering graphics is proposed based on an im-proved region nesting and a novel watermark generation. First, an intensive investigation is performed to the recently proposed re-gion nesting (RN) partition. It is found that the original vertex and its mapped one cannot be guaranteed to be on the same line, which means that it still has room for improvement in term of distortion. Based on this, an improved region nesting partition (IRN) is pro-posed. Secondly, inspiring by the idea of soldiers parading, a novel watermark generation based on the short hash of the adjacent ge-ometric features is developed. Then, a new coordinate system is constructed to achieve invariability of translation, scaling, rotation and entity re-arrangement. Based on the above techniques, a semi-fragile reversible watermarking for authenticating 2D engineering graphics is put forward. Experimental results and analysis show that IRN can obtain at least 15% distortion reduction compared with RN, the precision of tampering localization can be improved to a single vertex, and a certain semi-fragility in translation, scal-ing, rotation, and entity re-arrangement can be achieved. Further-more, it has no file size expansion, and can obtain a good balance among imperceptibility, semi-fragility and tampering localization precision.

Deep image hiding is a challenging image processing task that aims to hide a secret image into a cover image of equal size perfectly. How to improve the imperceptibility of deep image hiding while ensuring high computational efficiency is a primary challenge. Where imperceptibility means not being visually perceived while not being perceived by the steganalysis model. In this paper, we propose a novel deep image hiding framework called DIH-OAIN (Deep Image Hiding based on One-way Adversarial Invertible Networks) to address it. Firstly, an image cascade framework is introduced to extract image semantics and details with dual-resolution branches, and reduces computation complexity by balancing image resolution and model complexity. Secondly, a hidden probability guided module is designed to constrain the secret image to be hidden in the texture region, utilizing the image texture complexity as prior knowledge. The above two points can effectively improve visual imperceptibility. Finally, a one-way adversarial training strategy is proposed to enhance the model imperceptibility. A series of experimental results show that the proposed method is significantly improved in imperceptibility comparing to state-of-the-art deep image hiding algorithms, while maintaining a low computation complexity.