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DMC-Watermark: A backdoor richer watermark for dual identity verification by dynamic mask covering

Applied Intelligence 2025年第8期55卷 1-15页

作者： Zhu, Yujia Wang, Ruoxi Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Advanced Computing Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

With the increasing use of neural networks, the importance of copyright protection for these models has gained significant attention. Backdoor watermarking is one of the key methods for protecting copyright. However, on the one hand, most existing backdoor watermarks are triggered by visual images, making them easily detectable, and therefore vulnerable to various attacks. On the other hand, it is difficult for these methods to carry information related to the creator’s identity which can easily lead to fraudulent claims of ownership. These factors contribute to the vulnerability and limitations of backdoor watermarking. In this paper, we propose DMC-Watermark, a backdoor richer watermarking method that uses dynamic mask-covered image structures as triggers. Leveraging the semantic preservation of image structure in transformation attacks, we select image structure as triggers. Furthermore, we convert the author-related information into an array of color information and apply it as a mask to the extracted image structures, allowing it to serve as a second layer of verification during the validation phase to resist fraudulent claims of ownership. The final trigger pattern, embedded with author-related image structures, is applied to the selected images in the trigger set, generating a final trigger set that is trained together with clean samples to produce a protected model. The experiments show that the proposed DMC-Watermark performs well in terms of fidelity, invisibility, undetectability, functionality, dual verification and robustness on three different datasets and four representative DNNs, and it has wide applicability and excellent results in high-resolution images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image watermarking

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Successive Interference Cancellation-Enabled Timely Status Update in Linear Multi-Hop Wireless Networks

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5298-5311页

作者： Han, Xinhui Pan, Haoyuan Wang, Zhaorui Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Future Network of Intelligence Institute School of Science and Engineering Shenzhen518172 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen518060 China

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays. © 2002-2012 IEEE.

关键词： Markov chains

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Overlapping community-based fair influence maximization in social networks under open-source development model algorithm

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Artificial Intelligence Review 2025年第8期58卷 1-31页

作者： Wei, Pengcheng Yan, Bei Huang, Sixing Zhou, ZhiHong School of Artificial Intelligence Chongqing University of Education Chongqing400065 China School of Mathematics and Big Data Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Jilin Normal University Jilin Siping136000 China

The aim of Influence Maximization (IM) in social networks is to identify an optimal subset of users to maximize the spread of influence across the network. Fair Influence Maximization (FIM) develops the IM problem with the aim of equitable distribution of influence across communities and enhancing the fair propagation of information. Among the solutions for FIM, community-based techniques enhance performance by effectively capturing the structural properties and ensuring a more equitable influence spread. However, these techniques often ignore the overlapping nature of communities and suffer from a trade-off between complexity and fairness. With this motivation, this study handles the FIM based on Overlapping Community detection under optimization algorithms (FIMOC). FIMOC includes an overlapping community detection approach that can consider the importance of influential overlapping nodes in communities. Meanwhile, FIMOC uses a non-overlapping and overlapping node selection module based on communities to identify potential candidate nodes. Subsequently, FIMOC uses the Open-Source Development Model Algorithm (ODMA) as an optimization algorithm to identify the set of influential nodes. Our method considers the dynamic and overlapping nature of social communities, ensuring that the influence spread is not only maximized but also equitably distributed across diverse groups. By leveraging real‐world social networks, we demonstrate the effectiveness of our method compared to state-of-the-art methods through extensive experiments. The results show that our method achieves a more balanced influence spread, providing a fairer solution, while also enhancing the overall reach of information dissemination. © The Author(s) 2025.

关键词： Open access

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Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data

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Environmental science and Pollution Research 2025年第19期32卷 11931-11949页

作者： Jiang, Jingmin Zhang, Jiahua Wang, Xue Zhang, Shichao Kong, Delong Wang, Xiaopeng Ali, Shawkat Ullah, Hidayat Space Information and Big Earth Data Research Center School of Computer Science and Technology Qingdao University Qingdao266071 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challenges. Therefore, this study aims to provide an effective model for multi-crop classification using high-resolution remote sensing time-series data. We designed two deep learning models based on convolutional neural network-long short-term memory (CNN-LSTM) and bidirectional long short-term memory (Bi-LSTM). The monthly synthetic time series of the normalized difference vegetation index (NDVI) from Sentinel-2 data will be used as input features to extract the multi-crop planting area in Shandong province’s northwestern, southwestern, and eastern regions. The results showed that deep learning models achieved higher accuracy compared to the random forest (RF) and extreme gradient boosting (XGBoost) models, with CNN-LSTM achieving the highest overall accuracy of 96.48%. At the county level, the coefficients of determination (R2) for the CNN-LSTM model were 0.91 for wheat, 0.88 for maize, and 0.73 for spring cotton. This study demonstrates that the CNN-LSTM model combined with monthly synthetic time-series NDVI provides a feasible approach for accurately mapping high-resolution multi-crop planting areas and also contributes significantly to decision support and resource management in agricultural production. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning High-resolution Monthly synthetic time-series NDVI Multi-crop classification Remote sensing Sentinel- 2

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Highly Robust and Diverse Coverless Image Steganography Against Passive and Active Steganalysis

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2771-2787页

作者： Guo, Bobiao Ping, Ping Xu, Feng Hohai University College of Computer Science and Software Engineering Nanjing210098 China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing210098 China Jiangsu Ocean University School of Computer Engineering Lianyungang222005 China

To avoid the pixel modification traces left by steganography from being detected by passive steganalysis, and to prevent the hidden data from being destroyed by active steganalysis attacks, Coverless Image Steganography (CIS) that does not modify pixels has attracted widespread attention. However, most existing CIS methods are limited in their maximum capacity due to insufficient diversity in their hash sequences. In addition, these methods struggle to maintain high robustness against both geometric and non-geometric attacks simultaneously. To address these two issues, a new coverless image steganography method is proposed to enhance CIS methods’ applicability, security, and robustness in highly insecure networks. During the hiding process, hash sequences are generated by a SHA-256 algorithm that integrates inter-block and inter-channel fusion, providing higher diversity than other CIS methods. Consequently, the proposed CIS method achieves higher capacity on publicly available datasets. During the extraction process, an evaluation metric that combines visual and histogram similarity is designed to improve the accuracy of inverse image retrieval. The experimental results demonstrate that the proposed CIS method achieves capacity increases of 26.73% and 38.34% over other CIS methods on the VOC and COCO datasets, respectively. Moreover, this method exhibits nearly 100% robustness against common active steganalysis. © 2004-2012 IEEE.

关键词： Steganography Robustness Feature Extraction data Mining Image Retrieval Visualization Accuracy Software Indexes Histograms Coverless Image Steganography CIS Hash Generation Algorithm Inverse Image Retrieval Steganalysis High Robustness Robust Image Image Coding Hidden Information Retrieval Accuracy Visual Similarity COCO dataset Convolutional Neural Network Computational Complexity Robust Method Image dataset Bit Error Rate Types Of Attacks Auxiliary Information Scale Invariant Feature Transform Index database Histogram Bins Length Information Cover Image Military Intelligence Secret Information Center Crop Secure Transmission Salt And Pepper Noise XOR Operation Number Of Different Sequences JPEG Compression Generative Adversarial Networks Pixel Position Common Attacks

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Privacy Preserving Adjacency Query Supporting Homoionym Search over Medical Graph data in Cloud Computing 17th

Privacy Preserving Adjacency Query Supporting Homoionym Sear...

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17th International Conference on Cloud Computing, CLOUD 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Li, Hourong Tian, Yun Wu, Bin Shi, Jiaoli School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China

ISBN: (纸本)9783031771521

Cloud computing is widely used in all walks of life today. Massive amounts of medical graph data are being outsourced to cloud servers to reduce overhead. The untrustworthiness of cloud servers puts the sensitive information of outsourced graph data at risk. To eliminate this security risk, it is an effective method to encrypt sensitive data. The adjacent queries are frequently used and highly valuable in graph data operations, and the adjacency query supporting homoionym search will enlarge the query effect and improve the query function. When the medical graph data is encrypted and stored on the cloud server, the operation of the data becomes extremely difficult. In this article, we propose a scheme to implement the adjacency query supporting homoionym search in cloud computing (AQHS), which maintains search contents privacy. We use a stem extraction algorithm and the searchable encryption mechanism to build secure index, and then achieve the adjacency query. The security of our proposed scheme is verified by formal analysis, and the effectiveness of the scheme is verified by experimental analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Structured Query Language

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Cost-Effective Edge data Caching With Failure Tolerance and Popularity Awareness

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5357-5369页

作者： Luo, Ruikun Zhang, Zujia He, Qiang Xu, Mengxi Chen, Feifei Dai, Xiaohai Wu, Song Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China Deakin University School of Information Technology GeelongVIC3125 Australia

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existing edge data caching (EDC) methods prioritize popular data and aim to minimize users’ data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enable uncertainty-aware edge data caching (uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Attribute-Based Access Control Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2227-2242页

作者： Wang, Xiuhua Yu, Mengyang Wang, Yaohui Pi, Yinjia Xu, Peng Wang, Shuai Jin, Hai Han, Min Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Wuhan430074 China State Key Laboratory of Cryptology Beijing100878 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The burgeoning complexity of communication necessitates a high demand for security. Access control encryption is a promising primitive to meet the security demand but the bulk of its constructions rely on formulating the access control policy with identities. Attribute-based access control policy in attribute-based encryption (ABE) is known to be more expressive without relying on enumerating identities. We propose a generic framework to build attribute-based access control encryption from ciphertext-policy ABE. Our instantiations prioritize different emphases on expressiveness and efficiency. The first instantiation supports multi-valued AND-gate access control structures, while the second supports the linear-secret-sharing access structure. Both are prototyped with efficiency validated empirically. © 2024 IEEE.

关键词： Ciphertext

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PFed-NS: An Adaptive Personalized Federated Learning Scheme Through Neural Network Segmentation

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IEEE Transactions on computers 2025年第6期74卷 1936-1948页

作者： Liu, Yuchen He, Ligang Zhang, Zhigao Ren, Shenyuan University of Warwick Department of Computer Science CoventryCV4 7AL United Kingdom Wuhan University School of Computer Science Wuhan430072 China China Hubei Luojia Laboratory Wuhan430072 China Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing100044 China

Federated Learning (FL) is typically deployed in a client-server architecture, which makes the Edge-Cloud architecture an ideal backbone for FL. A significant challenge in this setup arises from the diverse data feature distributions across different edge locations (i.e., non-IID data). In response, Personalized Federated Learning (PFL) approaches have been developed. Network segmentation-based PFL is an important approach to achieving PFL, in which the training network is divided into a global segment for server aggregation and a local segment maintained client-side. Existing methods determine the segmentation before the training, and the segmentation remains fixed throughout the PFL training. However, our investigation reveals that model representations vary as PFL progresses and the fixed segmentation may not deliver best performance across various training settings. To address this, we propose PFed-NS, a PFL framework based on adaptive network segmentation. This adaptive segmentation technique is composed of two elements: a mechanism for assessing divergence of clients’ probability density functions constructed from network layers’ outputs, and a model for dynamically establishing divergence thresholds, beyond which server aggregation is deemed detrimental. Further optimization strategies are proposed to reduce the computation and communication costs incurred by divergence modeling. Moreover, we propose a divergence-based BN strategy to optimize BN performance for network segmentation-based PFL. Extensive experiments have been conducted to compare PFed-NS against recent PFL models. The results demonstrate its superiority in enhancing model accuracy and accelerating convergence. © 1968-2012 IEEE.

关键词： Probability density function

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A power optimization approach for mixed polarity Reed–Muller logic circuits based on multi-strategy fusion memetic algorithm

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Frontiers of Information Technology & Electronic Engineering 2025年第3期26卷 415-426页

作者： Mengyu ZHANG Zhenxue HE Yijin WANG Xiaojun ZHAO Xiaodan ZHANG Limin XIAO Xiang WANG Intelligent Agricultural Equipment Research Institute Hebei Agricultural UniversityBaoding 071001China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural UniversityBaoding 071001China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Electronic Information Engineering Beihang UniversityBeijing 100191China

The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization *** optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power ***,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation ***,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit *** results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization *** achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.

关键词： Power optimization Multi-strategy fusion memetic algorithm(MFMA) Mixed polarity Reed-Muller(MPRM) Combinatorial optimization problem

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