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Robust style injection for person image synthesis

CAAI Transactions on Intelligence technology 2025年第2期10卷 402-414页

作者： Yan Huang Jianjun Qian Shumin Zhu Jun Li Jian Yang School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina School of Fashion and Textiles The Hong Kong Polytechnic UniversityHong KongChina

Person Image Synthesis has been widely used in fashion with extensive application *** point of this task is how to synthesise person image from a single source image under arbitrary *** methods generate the person image with target pose well;however,they fail to preserve the fine style details of the source *** address this problem,a robust style injection(RSI)model is proposed,which is a coarse-to-fine framework to synthesise target the person *** develops a simple and efficient cross-attention based module to fuse the features of both source semantic styles and target pose for achieving the coarse aligned *** adaptive instance normalisation is employed to enhance the aligned features in conjunction with source semantic ***,source semantic styles are further injected into the positional normalisation scheme to avoid the fine style details erosion caused by massive *** training losses,optimal transport theory in the form of energy distance is introduced to constrain data distribution to refine the texture style ***,the authors’model is capable of editing the shape and texture of garments to the target style *** experiments demonstrate that the authors’RSI achieves better performance over the state-of-art methods.

关键词： computer vision image reconstruction virtual try‐on

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A secure double spectrum auction scheme

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Digital Communications and Networks 2024年第5期10卷 1415-1427页

作者： Jiaqi Wang Ning Lu Ziyang Gong Wenbo Shi Chang Choi School of Computer Science and Engineering Northeastern UniversityShenyang110004China School of Computer Science and Technology Xidian UniversityXi©anChina Dept.of Computer Engineering Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120G School of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum *** auctions came into being,which can effectively utilize spectrum *** of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be *** scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and *** begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction *** these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this *** traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum *** addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum ***,security analysis and performance evaluation of our protocol are discussed.

关键词： Secure double spectrum auction SGX technology Privacy information Ethereum platform Verification

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Robust federated contrastive recommender system against targeted model poisoning attack

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science China(Information sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Impact of asymptomatic infected individuals on epidemic transmission dynamics in multiplex networks with partial coupling

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Chinese Physics B 2024年第10期33卷 80-87页

作者： Xin Hu Jiaxing Chen Chengyi Xia School of Control Science and Engineering Tiangong UniversityTianjin 300387China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China School of Artificial Intelligence Tiangong UniversityTianjin 300387China

The theory of network science has attracted great interest of many researchers in the realm of biomathematics and public health,and numerous valuable epidemic models have been *** previous studies,it is common to set up a one-to-one correspondence between the nodes of a multi-layer network,ignoring the more complex situations in *** the present work,we explore this situation by setting up a partially coupled model of a two-layer network and investigating the impact of asymptomatic infected individuals on *** propose a self-discovery mechanism for asymptomatic infected individuals,taking into account situations such as nucleic acid testing in the community and individuals performing self-antigen testing during the *** these factors together,through the microscopic Markov chain approach(MMCA)and extensive Monte Carlo(MC)numerical simulations,we find that the greater the coupling between the networks,the more information dissemination is *** order to control the epidemics,more asymptomatic infected individuals should be made aware of their *** adoption of nucleic acid testing and individual adoption of antigenic self-testing can help to contain epidemic ***,the epidemic threshold of the proposed model is derived,and then miscellaneous factors affecting the epidemic threshold are also *** results are conducive to devising the prevention and control policies of pandemics.

关键词： asymptomatic infected individuals multi-layer networks partial interdependence

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A General Linguistic Steganalysis Framework Using Multi-Task Learning

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computer Systems science & engineering 2023年第8期46卷 2383-2399页

作者： Lingyun Xiang Rong Wang Yuhang Liu Yangfan Liu Lina Tan School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK School of Computer Science Hunan University of Technology and BusinessChangsha410205China

Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary *** it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts *** this paper,we propose a general linguistic steganalysis framework named LS-MTL,which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic ***-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed *** the proposed framework,convolutional neural networks(CNNs)are utilized as private base models to extract sensitive features for each steganalysis ***,a shared CNN is built to capture potential interaction information and share linguistic features among all ***,LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or *** results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task,while average Acc,Pre,and Rec are increased by 0.5%,1.4%,and 0.4%,*** ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.

关键词： Linguistic steganalysis multi-task learning convolutional neural network(CNN) feature extraction detection performance

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Write-Once-Read-Many-Times Nonvolatile Memory Characteristics of Sol-Gel Hafnium Zirconium Oxide

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IEEE Transactions on Electron Devices 2024年第5期71卷 3285-3292页

作者： Jhang, Wun-Ciang Hsieh, Tsung-Chun Zhang, Xuan-Zhi Qiu, Zi-Rong Hsu, Chih-Chieh Joodaki, Mojtaba National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology The Graduate School of Electronic Engineering Douliu64002 Taiwan Constructor University School of Computer Science and Engineering Bremen28759 Germany

In this article, write-once-read-many-times (WORM) memory behavior of HfZrO (HZO) ferroelectric material is demonstrated. A stoichiometric Hf0.5Zr0.5O2 thin film prepared using a sol-gel process is used as a resistive switching (RS) layer. The top and bottom electrodes are Al and n+-Si, respectively. The Al/HZO/n+-Si memory shows a low current of 10-11 A at a read voltage of 0.6 V in the OFF state. A writing process occurs when the voltage on Al electrode increases to ∼2.8 V. The ratio of the OFF-state resistance (Roff) to ON-state resistance (Ron) is 106. Both data retention and stress tests indicate that Roff and Ron can remain the same for over 104s. Moreover, data stability is further confirmed at an elevated temperature of 85 °C. The switching speed of the HZO memory is 40 ns, corresponding to a power consumption of 0.98 nJ. The RS mechanism is investigated, and the carrier conduction mechanisms are studied by the analysis of current-voltage characteristics at different temperatures. © 1963-2012 IEEE.

关键词： X ray scattering

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Quantized Control for Input-to-State Stabilization of Discrete-Time Markov Jump Systems with Coding and Decoding Procedures

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IAENG International Journal of Applied Mathematics 2025年第1期55卷 26-33页

作者： Gao, Xiaohui Su, Yue Han, Chengyi Han, Jing Chen, Yebin School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Electrical and Information Engineering Wanjiang University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China

This paper investigates the input-to-state stabilization of discrete-time Markov jump systems. A quantized control scheme that includes coding and decoding procedures is proposed. The relationship between the error in the system state before and after encoding and decoding, the quantization range, and the packet length is established. A criterion for inputto- state stability of the quantized closed-loop Markov jump system is obtained using a Lyapunov function and the Schur complement. The gains of the required quantized controller can be derived from a feasible solution to linear matrix inequalities. Finally, the proposed control scheme is validated using an operational amplifier circuit system. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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A novel shilling attack on black-box recommendation systems for multiple targets

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Neural Computing and Applications 2025年第5期37卷 3399-3417页

作者： Liu, Shuangyu Yu, Siyang Li, Huan Yang, Zhibang Duan, Mingxing Liao, Xiangke School of Information Science and Engineering Hunan University Hunan Changsha410082 China College of Information Technology and Management Hunan University of Finance and Economics Hunan Changsha410000 China Hunan Province Key Laboratory of Industrial Internet Technology and Security Changsha University Hunan Changsha410022 China Shenzhen Institute Hunan University Guangdong Shenzhen518063 China School of Computer Science National University of Defense Technology Hunan Changsha410073 China

In the digital era, e-platforms ubiquitously deploy recommendation systems, utilizing machine learning paradigms to tailor content according to user preferences and needs. Yet, the integrity of these systems is often compromised by shilling attacks, where malicious entities inject fake user profiles to skew product exposure and sales. This vulnerability has catalyzed research efforts to bolster the robustness and security of recommendation systems, primarily through controlled attacks and reinforcement training. This paper introduces an innovative approach to combat the inefficiency of traditional single-target shilling attacks. We propose a novel multi-target shilling strategy for black-box recommendation systems, capable of generating convincing and aggressive fake user profiles. Given the complexity of the recommendation system algorithms, we employ surrogate models to replicate and understand the target system’s behavior. The surrogate model is then subjected to attacks using our proposed methodology. An attack is deemed successful when it achieves an 80% or higher success rate against the surrogate, preluding an attack on the actual recommendation system. Our model, RWA-GAN, stands out by integrating two components: a generative adversarial network (GAN) for crafting realistic fake user profiles, and a mechanism to enhance attack success rates. This dual approach not only expedites the attack process but also ensures the generation of more authentic and well-distributed fake profiles, making the model easier to migrate. Additionally, the paper delineates several defense strategies against such attacks, contributing significantly to the discourse on enhancing the robustness of recommender systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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Enhancing anemia detection through multimodal data fusion: a non-invasive approach using EHRs and conjunctiva images

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Discover Artificial Intelligence 2024年第1期4卷 1-19页

作者： Ramzan, Muhammad Saeed, Muhammad Usman Ali, Ghulam School of Computer Science and Engineering Central South University Hunan Changsha410017 China Department of Computer Science University of Okara Punjab Okara56300 Pakistan

Anemia detection using multimodal approaches leverages the integration of multiple data sources, such as imaging, clinical records, and hematological parameters, to improve diagnostic accuracy. Such methods can capture the complex interplay of factors contributing to anemia, providing a more comprehensive assessment than traditional single-modality techniques. In this research, a novel deep learning multi-modal feature fusion approach is proposed for the automated detection of anemia using EHRs (Electronic Health Records), and Conjunctiva image dataset. First, EHR records are preporcessed by selecting the most appropriate features using Random Forest. The features from the conjunctiva images are extracted using RCBAM (Reverse Convolution Block Attention Mechanism). After that, GRAD-Cam algorithm is applied to calculate the pixel percentages of all the features. The output of Random Forest and GRAD-Cam algorithms is concatenated to form a multimodal fusion. The important information from the concatenated features is selected with the help of a professional healthcare consultant. The different experiments are performed on textual and image datasets individually and after concatenating. The results show that the proposed model outperforms from state-of-the-art methods with an accuracy of 95%. Despite challenges such as class imbalance and computational demands, our findings reveal substantial clinical potential, offering a patient-friendly and accessible diagnostic solution. © The Author(s) 2024.

关键词： Electronic health record

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