This work proposes an On-Off Keying (OOK) coding scheme for covert communication over complex Gaussian channels. In particular, a transmitter Alice employs phase deflection to covertly transmit information to a receiver Bob, simultaneously ensuring that the communication intent is concealed from a warden Willie. The utilization of phase deflection allows Alice to improve the transmission rate by leveraging Willie’s uncertainty about the received phase, without changing the codebook construction. Considering the asymmetry of the OOK codebook’s input distribution and shape constellation, we first analyze the relationship between the input distribution and the signal amplitude, and then propose a scheme that can achieve covert transmission with the input distribution of the “on” symbol$a_{n}=\mathcal {O}\left ({{\frac {1}{\sqrt {n}}}}\right)$and an average transmission power$\beta ^{2}=\mathcal {O}({1})$. We quantify the improvement brought from the phase resource as phase deflection gain and derive its closed-form expression by approximating the Kullback-Leibler (KL) divergence and mutual information through Taylor expansion. Numerical results show that our scheme achieves significant phase deflection gain, and the maximum gain can be achieved by fully utilizing the phase resources through three stages.

Unmanned aerial vehicles (UAVs) are viewed as a key component of 5G, 6G and beyond wireless networks to receive, store and forward information. Benefiting from swift deployment, low cost and high mobility, it has become a promising trend to leverage the UAVs to assist the covert communicator (Alice) against the detector’s (Willie’s) detection in a covert communication network. Nevertheless, once Willie notices the covert transmission process, the covert information is exposed to the risk of being cracked. In this paper, we propose a UAV-assisted covert communication asymmetric information game model (UCCAIG) that focuses on the covert communication countermeasure process based on information asymmetry. We introduce the UAV into covert communication relay assistant Alice and interference assistant Willie respectively, analyze the interaction between Alice and Willie, and derive the optimal strategies for both sides. We further prove the existence of a Bayesian Nash equilibrium (BNE) in the UCCAIG. In addition, we evaluate our proposals on a covert communication testbed with software radio, and the numerical results show that our proposed strategy increases the payoffs of Alice, reduces the payoffs of Willie in a variety of scenarios, and improves the covert communication rate compared with UAV-relayed game (URG).

This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. We also provide a newly collected dataset with 3D ground truth to validate the proposed approach. The dataset and code will be released at https://dihuangdh.github.io/hhor.

Abstract The quality of service (QoS) is a momentous performance index of wireless ad hoc networks. However, unreasonable channel allocation will degrade the QoS. During the process of channel allocation, if user requests for idle channels are rejected, the QoS will lower accordingly. In this paper, we propose a dynamic channel allocation strategy for QoS enhancement to enhance the QoS and reduce the rejection rate of idle channel requests. First of all, the channel availability model is modeled as a Continuous Time Markov Chain in a bid to calculate the channel availability which is used to compute the channel availability factor (CAF) and the channel availability threshold (CAT). Then, the dynamic channel allocation process is achieved based on the comparison result of CAF and CAT. Ultimately, the QoS is guaranteed by exploiting the earliest deadline first algorithm to forward packets and enhanced by using the packet segmentation algorithm to divide packets into packet flows. The simulation results prove that the proposed channel allocation strategy increases the packet delivery ratio and throughput, decreases the end‐to‐end delay and total overhead and shows a QoS enhancement than existing schemes.

Software-defined networking (SDN) has become a promising trend for managing the Industrial Internet of Things (IIoT) devices. As the core of sensitive data storage and business interaction, the SDN is vulnerable to advanced persistent threats (APTs) attacks, while honeypots have shown great promise against APT attacks. In this article, we propose a new SDN-based dynamic bounded rational honeypot-APT game model in IIoT. Specifically, the defender maximizes the utility by chossing the period strategy of honeypot collecting and analyzing the data, while the attacker maximizes utility by choosing the period strategy of its latency and attack. To describe the bounded rationality, we model the simultaneous dynamic attack and defense process through the prospect theory, in which the Prelec function and the value function are both introduced. Experiment results show that bounded rationality affects strategy selection and reduces defender and attacker’s utilities. Furthermore, our strategy outperforms the existing work in defensive performance.

Recovering human pose from RGB images and videos has drawn increasing attention in recent years owing to minimum sensor requirements and applicability in diverse fields such as human-computer interaction, robotics, video analytics, and augmented reality. Although a large amount of work has been devoted to this field, 3D human pose estimation based on monocular images or videos remains a very challenging task due to a variety of difficulties such as depth ambiguities, occlusion, background clutters, and lack of training data. In this survey, we summarize recent advances in monocular 3D human pose estimation. We provide a general taxonomy to cover existing approaches and analyze their capabilities and limitations. We also present a summary of extensively used datasets and metrics, and provide a quantitative comparison of some representative methods. Finally, we conclude with a discussion on realistic challenges and open problems for future research directions.

Due to the advantages of retrieval speed and storage space, deep hashing methods have become a research hotspot in the field of large-scale image retrieval. Most of existing deep hashing methods pay close attention to similarity between images without images at the top of the ranking list similar to query targets. In the paper, a novel deep hashing model is proposed to preserve top images similar to the query images and optimize the quality of hash codes for image retrieval. Specifically, the optimized AlexNet is utilized to extract discriminative image representations and learn hashing functions simultaneously. The loss function based on acceleration strategy is designed to ensure similarity between returned images at the top of the ranking list and query images. In addition, we implement the model training in a batch-process fashion to low the image storage. Moreover, our extensive experiments on standard benchmarks demonstrate that our method outperforms several state-of-the-art deep hashing methods.

Wireless covert channel is an emerging covert communication technique which conceals the very existence of secret information in wireless signal including GSM, CDMA, and LTE. The secret message bits are always modulated into artificial noise superposed with cover signal, which is then demodulated with the shared codebook at the receiver. In this paper, we first extend the traditional KS test and regularity test in covert timing channel detection into wireless covert channel, which can be used to reveal the very existence of secret data in wireless covert channel from the aspect of multiorder statistics. In order to improve the undetectability, a wireless covert channel for OFDM-based communication system based on constellation shaping modulation is proposed, which generates additional constellation points around the standard points in normal constellations. The carrier signal is then modulated with the dirty constellation and the secret message bits are represented by the selection mode of the additional constellation points; shaping modulation is employed to keep the distribution of constellation errors unchanged. Experimental results show that the proposed wireless covert channel scheme can resist various statistical detections. The communication reliability under typical interference is also proved.

Network flow watermarking (NFW) is an emerging flow correlation technique to deanonymize an anonymous communication system or detect stepping stones, in which a watermark is encoded into a network flow by manipulating some flow characteristics, predominantly by altering timing information. Although interval-based NFWs that employ time intervals as carrier have proven to be capable of resisting moderate network interference, they are vulnerable to some statistic-based attacks, which may expose the very existence of watermark and enable attackers to damage or remove watermark from the observed flow. In this study, using insider swapping of time intervals and an adaptive centroid quantization framework, we design a highly invisible NFW scheme, which is undetectable by multi-flow attacks (MFA), Kullback-Leibler divergence (KLD) test, Kolmogorov-Smirnov (K-S) test, and spread spectrum flow watermark (SSFW) detection. Experimental results using real traffic and public dataset show that the proposed NFW scheme can outperform three typical NFW schemes on invisibility while maintaining a strong interference-resistance capability of network jitter, packet loss, and dummy packet insertion.

With the promptly growing population of the low-cost Microsoft Kinect sensor, action recognition, which is a hard yet important problem in computer vision, has been received substantial attention. However, most existing approaches in action recognition spend much time on feature detection even though these methods can achieve high recognition rates. In this paper, we propose a local mean spatio-temporal feature (LMSF) to speed up depth image based action recognition. In particular, we solve the problem from three aspects: (1) associate the 4D normals by a local mean spatio-temporal neighborhood; (2) extract motion frames by detecting the differences between consecutive frames; (3) reduce redundant normals extracted from depth cloud points by sparse coding. The proposed approach is tested on two public benchmark datasets, i.e., MSRAction3D and MSRGesture3D. Experimental results demonstrate the advantages of our improvement method and the state-of-the-art performance on processing speed.