Video information hiding and transmission over noisy channels leads to errors on video and degradation of the visual quality notably. In this paper, a video signal fusion scheme is proposed to combine sensed host signal and the hidden signal with quantization index modulation (QIM) technology in the compressive sensing (CS) and discrete cosine transform (DCT) domain. With quantization based signal fusion, a realistic solution is provided to the receiver, which can improve the reconstruction video quality without requiring significant extra channel resource. The extensive experiments have shown that the proposed scheme can effectively achieve the better trade-off between robustness and statistical invisibility for video information hiding communication. This will be extremely important for low-resolution video analytics and protection in big data era

Conventional image steganalysis mainly focus on presence detection rather than the recovery of the original secret messages that were embedded in the host image. To address this issue, we propose an image steganalysis method featured in the compressive sensing (CS) domain, where block CS measurement matrix senses the transform coefficients of stego-image to reflect the statistical differences between the cover and stego-images. With multi-hypothesis prediction in the CS domain, the reconstruction of hidden signals is achieved efficiently. Extensive experiments have been carried out on five diverse image databases and benchmarked with four typical stegographic algorithms. The comprehensive results have demonstrated the efficacy of the proposed approach as a universal scheme for effective detection of stegography in secure communications whilst it has greatly reduced the numbers of features requested for secret signal reconstruction.

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<abstract> <p>Set-valued data is extremely important and widely used in sensor technology and application. Recently, privacy protection for set-valued data under differential privacy (DP) has become a research hotspot. However, the DP model assumes that the data center is trustworthy, consequently, increasingly attention has been paid to the application of the local differential privacy model (LDP) for set-valued data. Constrained by the local differential privacy model, most methods randomly respond to the subset of set-valued data, and the data collector conducts statistics on the received data. There are two main problems with this kind of method: one is that the utility function used in the random response loses too much information; the other is that the privacy protection of the set-valued data category is usually ignored. To solve these problems, this paper proposes a set-valued data collection method (SetLDP) based on the category hierarchy under the local differential privacy model. The core idea is to first make a random response to the existence of the category, continue to disturb the item count if the category exists, and finally randomly respond to a candidate itemset based on the new utility function. Theory analysis and experimental results show that the SetLDP can not only preserve more information, but also protect the category private information in set-valued data.</p> </abstract>

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In order to solve the problems of information sharing, tampering and leaking in mechanical operation data acquisition, an abnormal data acquisition system for mechanical operation based on block chain technology was designed. The system takes Beihang Chain as the prototype and designs the overall architecture of the system. Data acquisition module uses data acquisition card, CPU, programmable logic device, A/D conversion chip and other equipment to collect and process abnormal data in mechanical operation. The alarm module divides the abnormal data collected by the data acquisition module into five levels: P1-P5. After the implementation of the alarm module, the abnormal information and alarm information in the process of mechanical operation are transmitted to the abnormal data management module for storage. The system adopts the method of mechanical operation anomaly data acquisition based on sparse sampling, and adopts hierarchical clustering method to establish the data acquisition tree of mechanical operation anomaly. The block chain technology is used to design the process of storing and monitoring abnormal data of mechanical operation. The experimental results show that the system has high accuracy of fitting curve for abnormal data acquisition, low real-time energy consumption, and the minimum energy consumption is only 0.01/10-3 J.

This paper optimizes the encoding of verifying G(p) and G(p→F(q)) which are two important and frequently used modal operators in optimization of encoding for bounded model checking (BMC). Through analysis of the properties of finite state machine (FSM) and LTL (linear-time temporal logic) when verifying these modal operators, it presents a concise recursive formula, which can efficiently translate BMC instances into SAT (satisfiability) instances. The logical properties of these recursion formulas are verified. The experimental comparison between the optimization of BMC and the other two important methods AA_BMC and Timo_BMC for solving these modal operators in BMC shows that the former is superior to the latter in both the scale of instances and the difficulty to solve the problem. Research of this paper is also beneficial to encoding optimization of verifying other modal operators in BMC.