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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Secure Rate-Splitting Multiple Access Transmissions in LMS Systems

arXiv

引用

arXiv 2023年

作者： He, Minjue Zhao, Hui Miao, Xiaqing Wang, Shuai Pan, Gaofeng Science and Technology on Electronic Information Control Laboratory Chengdu610043 China Communication Systems Department EURECOM Sophia Antipolis06410 France School of Information and Electronics Beijing Institute of Technology Beijing100081 China

This letter investigates the secure delivery performance of the rate-splitting multiple access scheme in land mobile satellite (LMS) systems, considering that the private messages intended by a terminal can be eavesdropped by any others from the broadcast signals. Specifically, the considered system has an N-antenna satellite and numerous single-antenna land users. Maximum ratio transmission (MRT) and matched-filtering (MF) precoding techniques are adopted at the satellite separately for the common messages (CMs) and for the private messages (PMs), which are both implemented based on the estimated LMS channels suffering from the Shadowed-Rician fading. Then, closed-form expressions are derived for the ergodic rates for decoding the CM, and for decoding the PM at the intended user respectively, and more importantly, we also derive the ergodic secrecy rate against eavesdropping. Finally, numerical results are provided to validate the correctness of the proposed analysis models, as well as to show some interesting comparisons. Copyright © 2023, The Authors. All rights reserved.

关键词： Decoding

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Research on Lightweight Sensing technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年第12期12卷 20678-20694页

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Scienc and Engineering Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering Nanjing210096 China Southeast University State Key Laboratory of Mobile Communication Nanjing210096 China

Channel State information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This paper concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) The development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multi-antenna techniques for single-antenna settings;2) The enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multi-antenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and PEI features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： communication channels (information theory)

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RainGaugeNet: CSI-Based Sub-6 GHz Rainfall Attenuation Measurement and Classification for ISAC Applications

arXiv

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arXiv 2025年

作者： Li, Yan Yang, Jie Huang, Yixuan Yang, Tao Wen, Chao-Kai Jin, Shi The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Frontiers Science Center for Mobile Information Communication and Security Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China The National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety Institute of Water Science and Technology Hohai University Nanjing210098 China The Institute of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan The National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model that leverages multipath and temporal features. Using only 20 seconds of CSI data, RainGaugeNet achieved over 90% classification accuracy in line-of-sight scenarios and over 85% in non-line-of-sight scenarios, significantly outperforming state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel state information

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Research On Multi - Target Data Association and Location Algorithm Based On Passive Multi - Sensor System

Research On Multi - Target Data Association and Location Alg...

引用

International Conference on Wavelet Active Media technology and information Processing (ICWAMTIP)

作者： Yao Siyi Xu Jianliang Shen Weiguo Wang Li Li Wanchun School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Communication Information Security Control Laboratory Jiaxing PRC

ISBN: (纸本)9781665413657

In this paper, the multi-target positioning research is carried out on the basis of a passive multi-sensor reconnaissance system, which requires an algorithm to solve the problem of multi-target data association and positioning in the presence of false alarms and missed detections (also named non-ideal). This paper proposes a new data association and localization algorithm. This new algorithm adopts the method of hierarchical association of data, achieves data association through coarse association and fine association, and finally deletes redundant targets through backtracking. The simulation results show the effectiveness and stability of the algorithm.

关键词： Location awareness Correlation Simulation Reconnaissance Power system stability Media Stability analysis

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GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network

arXiv

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arXiv 2022年

作者： Zhou, Li Li, Yujun Li, Jin Huang, Minhuan Nie, Yuanping Liu, Yiwei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Science and Technology on Information System Security Chengdu China National Key Laboratory of Science and Technology on Information System Security Beijing China

With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on code review done by experts, and how to automatedly detect software vulnerabilities is an open problem so far. In this paper, we propose a novel solution named GraphEye to identify whether a function of C/C++ code has vulnerabilities, which can greatly alleviate the burden of code auditors. GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality. Hence, detecting vulnerable functions is attributed to the graph classification *** is comprised of VecCPG and GcGAT. VecCPG is a vectorization for the code property graph, which is proposed to characterize the key syntax and semantic features of the corresponding source code. GcGAT is a deep learning model based on the graph attention graph, which is proposed to solve the graph classification problem according to VecCPG. Finally, GraphEye is verified by the SARD Stack-based Buffer Overflow, Divide-Zero, Null Pointer Deference, Buffer Error, and Resource Error datasets, the corresponding F1 scores are 95.6%, 95.6%,96.1%,92.6%, and 96.1% respectively, which validate the effectiveness of the proposed solution. © 2022, CC BY.

关键词： Deep learning

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Deep transfer clustering of radio signals

arXiv

引用

arXiv 2021年

作者： Xuan, Qi Li, Xiaohui Chen, Zhuangzhi Xu, Dongwei Zheng, Shilian Yang, Xiaoniu The Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Modulation recognition is an important task in radio signal processing. Most of the current researches focus on supervised learning. However, in many real scenarios, it is difficult and cost to obtain the labels of signals. In this letter, we turn to the more challenging problem: can we cluster the modulation types just based on a large number of unlabeled radio signals? If this problem can be solved, we then can also recognize modulation types by manually labeling a very small number of samples. To answer this problem, we propose a deep transfer clustering (DTC) model. DTC naturally integrates feature learning and deep clustering, and further adopts a transfer learning mechanism to improve the feature extraction ability of an embedded convolutional neural network (CNN) model. The experiments validate that our DTC significantly outperforms a number of baselines, achieving the state-of-the-art performance in clustering radio signals for modulation recognition. © 2021, CC BY.

关键词： Modulation

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

引用

Computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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Adaptive visibility graph neural network and it's application in modulation classification

arXiv

引用

arXiv 2021年

作者： Xuan, Qi Qiu, Kunfeng Zhou, Jinchao Chen, Zhuangzhi Xu, Dongwei Zheng, Shilian Yang, Xiaoniu The Institute of Cyberspace Security The College of Information Engineering Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China The Institute of Cyberspace Security Zhejiang University of Technology Hangzhou310023 China The Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China

Our digital world is full of time series and graphs which capture the various aspects of many complex systems. Traditionally, there are respective methods in processing these two different types of data, e.g., Recurrent Neural Network (RNN) and Graph Neural Network (GNN), while in recent years, time series could be mapped to graphs by using the techniques such as Visibility Graph (VG), so that researchers can use graph algorithms to mine the knowledge in time series. Such mapping methods establish a bridge between time series and graphs, and have high potential to facilitate the analysis of various real-world time series. However, the VG method and its variants are just based on fixed rules and thus lack of flexibility, largely limiting their application in reality. In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AVGNet, by utilizing GNN model DiffPool as the classifier. We then adopt AVGNet for radio signal modulation classification which is an important task in the field of wireless communication. The simulations validate that AVGNet outperforms a series of advanced deep learning methods, achieving the state-of-the-art performance in this task. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

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Path Planning for Mars Rovers Using Constraint-Based Approach on Path Length and Turning Radius

SSRN

引用

SSRN 2023年

作者： Dai, Juan Li, Jianfeng Zhu, Shengtao Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China School of Automation Beijing Information Science & Technology University Beijing100192 China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing100101 China

An improved dynamic window approach is proposed for local path planning of the Mars rover based on its constraint characteristics and kinematic model. The dynamic window algorithm is commonly utilized in intelligent rover obstacle avoidance and unmanned driving due to its simple principle and easy implementation. The proposed approach can effectively satisfy the dynamic obstacle avoidance requirements of the Mars rover. However, the traditional dynamic window method may suffer from local motion dead zones and longplanned paths. To resolve the issue of dynamic obstacle avoidance for the Mars rover, a length limitation vector is proposed to alleviate the problem of motion dead zone and enable dynamic obstacle recognition. Moreover, a new dynamic window space is generated by incorporating a turning radius constraint to accommodate the Mars rover’s large turning radius. The evaluation function is optimized to achieve a safe, efficient, and smooth path plan for the Mars rover. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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