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With the enhancement of people's security awareness, transmitting secret information securely has gradually become a demand for the public. Steganography is a technology of representing secret information within another carrier, aiming at transmitting secret information without causing suspicion. Most of the traditional steganographic algorithms hide secret information by modifying the statistical characteristics, which will leave traces to the carriers. Although these modifications are too tiny to be distinguished by human eyes, they can be detected by steganalysis algorithms. Differently, coverless steganography, also written as steganography without embedding, accomplish the process of information hiding by constructing the relationship between secret information and carriers. Due to no modification to the carriers, all of the steganalysis algorithms are expired. In this paper, more than 90 papers are included to provide a review in coverless steganographic algorithms, covering the major development process of coverless image and video steganographic algorithms. The main contribution of the existing methods is summarized. Besides, the current general issues of capacity, robustness, and security are discussed adequately for both image and video algorithms. Especially, the security of coverless steganography is discussed for the first time from theoretical analysis to actual investigation in this review. [ABSTRACT FROM AUTHOR]

In the research of complex networks, how to find a set of nodes in the network with the most extensive range in the propagation process, i.e., the Influence Maximization (IM) problem, is one of the focal topics. Existing studies mainly consider the information dissemination process on networks and how to select diffusive nodes efficiently, but little attention has been paid to changes related to the network structure. In reality, networked systems are exposed to uncertain interferences and even destructive sabotages, and cascading failures are one common destruction that can cause networks to collapse even if only a small number of nodes fail. In the case of various complex environmental factors, how to select robust and influential nodes, i.e., the robust influence maximization (RIM) problem, is of great importance in promoting the realistic application of the influence maximization problem. This paper investigates the RIM problem under cascading failures to address the shortcomings in previous studies. Based on existing research, a new performance evaluation metric, R S-cf , is designed to assess the level of robust influence in a numerical form. For solving the seed determination problem, a Memetic algorithm towards the RIM problem under cascading failures, MA-RIM CF , is designed to find nodes with stable information propagation capability guided by R S-cf. Experiments have been conducted on both synthetic and realistic networks to validate the performance of the algorithm. Results indicate that MA-RIM CF can obtain competitive candidates over existing approaches, and seeds with robust and influential abilities are generated to solve diffusion dilemmas. [ABSTRACT FROM AUTHOR]