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HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10144-10156页

作者： Zhao, Minghua Zheng, Wen Hu, Jing Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an710048 China

Hyperspectral anomaly detection (HAD) identifies anomalies by analyzing differences between anomalies and background pixels without prior information, presenting a significant challenge. Most existing studies leverage the high correlation in spectral and spatial dimensions, primarily focusing on local spectral and spatial information for background reconstruction while neglecting long-range dependencies. This local perception constrains models from fully capturing intrinsic spatial–spectral connections. To address this, we propose a novel hybrid transformer-CNN network for HAD (HTC-HAD). Specifically, HTC-HAD combines CNNs with transformers, where the CNN focuses on local modeling, and the transformer addresses long-range modeling. This dual approach ensures the accurate reconstruction of backgrounds by capturing both local and long-range dependencies. Meanwhile, to reduce model complexity and redundancy among neighboring bands, we incorporate a simplified and effective band selection strategy as preprocessing. In addition, to prevent anomalies from being reconstructed during background estimation, we employ an adaptive weight loss function to suppress them. Experimental results on several real datasets, both qualitatively and quantitatively, demonstrate that our HTC-HAD achieves satisfying detection performance. © 2008-2012 IEEE.

关键词： Anomaly detection

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Diffusion Model with Multi-layer Wavelet Transform for Low-Light Image Enhancement

Diffusion Model with Multi-layer Wavelet Transform for Low-L...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology 710048 China Shaanxi Key Laboratory for Network Computing and Security Technology 710048 China

ISBN: (纸本)9798350368741

Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up- and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture;it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://***/lalalulao/MWT-Diff. © 2025 IEEE.

关键词： Diffusion Model Low-light Image Enhancement Multi-layer Wavelet Transform Soft Gate Mechanism

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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Textual Data De-Privatization Scheme Based on Generative Adversarial networks 24th

Textual Data De-Privatization Scheme Based on Generative Adv...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Du, Yanning Xu, Jinnan Zhang, Yaling Wang, Yichuan Wang, Zhoukai School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819615506

In many fields, such as healthcare, finance, and scientific research, data sharing and collaboration are critical to achieving better outcomes. However, the sharing of personal data often involves privacy risks, so privacy-preserving techniques are needed to ensure data security and privacy. The superior performance and flexibility of generative models in data representation have led to significant progress in the development of data privacy. This paper proposes a textual data de-privatization scheme based on generative adversarial networks that well combines generative adversarial networks with privacy protection. The generated de-privacy data retains the statistical properties of the original data and effectively removes sensitive information. Moreover, feature sorting and reshaping modules are introduced to enable the generator to better capture the relationships between features, thus improving the quality of synthetic data. In this paper, the utility of synthetic data is evaluated from three aspects, including similarity assessment, privacy assessment, and model utility assessment. Experimental results show that this method achieves a good trade-off in terms of textual data privacy protection and data quality maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G networks

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 748-781页

作者： Zhu, Xiaoqiang Liu, Jiqiang Lu, Lingyun Zhang, Tao Qiu, Tie Wang, Chunpeng Liu, Yuan Beijing Jiaotong University School of Software Engineering Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University College of Intelligence and Computing Tianjin300350 China Qilu University of Technology School of Cyber Security and the Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250353 China

The rapid growth of intelligent sensing capabilities and super computation power in 6G mobile communication systems has facilitated their application in diverse domains such as smart health, smart factories, and the industrial Internet of Things. Integrated Sensing and Communication (ISAC), as a core technology, has merged with artificial intelligence (AI) to enable intelligent connectivity, leading to a paradigm shift in traditional communication modes. This paper presents a visionary design for an ISAC-oriented unified IoT architecture that integrates software-defined communication and super-intelligent agents. By leveraging dynamic adaptability, self-learning, and optimization, the ISAC system can intelligently and flexibly respond to evolving requirements and environments. The architecture is redefined into three layers: the hardware layer, the omniscient layer, and the application layer. Furthermore, a retrospective survey of ISAC technology development over the past decade is conducted, highlighting new design principles for AI-empowered networks and multi-modals that support "intelligent connectivity" across various application scenarios and reinforce the security of ISAC. This paper categorizes the related works according to the different layer structures of the proposed architecture, and some important physical and machine learning models are introduced. Additionally, we summarize the current technological bottlenecks associated with ISAC and propose future research directions and potential solutions that lay the foundation for the future development of secure and intelligent communication networks. © 1998-2012 IEEE.

关键词： Computer architecture

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Hedonic Coalition Formation Game and Contract-Based Federated Learning in AAV-Assisted Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 11258-11272页

作者： Liu, Jia Li, Xiaoxu Xu, Yang Lyu, Chen Wang, Yichuan Liu, Xiaoying Xidian University School of Computer Science and Technology Xi’an710071 China Shanghai University of Finance and Economics School of Computing and Artificial Intelligence MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai200433 China Xi’an University of Technology Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an710071 China Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Things (IoT) components enable effective artificial intelligence (AI)-based models to be built. Beyond ground data sources, autonomous aerial vehicles (AAVs)-based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), have become increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned AAVs. To this end, we propose in this article a federated learning (FL)-based architecture that enables privacy-preserving collaborative machine learning across a federation of independent DaaS providers for the development of IoT applications. Specifically, this work introduces a novel incentive mechanism based on the hedonic coalition formation game to enhance the sustainable efficiency and stability of the FL system. By establishing tailored operational rules and functions, the proposed mechanism enables IoT sensing nodes to autonomously form optimal coalitions with AAVs, thereby ensuring robust collaboration. To deal with incentive mismatches and information asymmetry, we leverage the contract theory and propose a self-disclosure mechanism that guarantees truthful reporting of AAV capabilities while optimizing the global model owner’s profits. The performance-based AAV type is also defined to offer a practical measure for heterogeneous AAVs and serve as the foundation for fair and effective contract design. Simulation results validate the superiority of the proposed approach, demonstrating significant improvements in utility optimization and system stability compared to existing benchmarks. © 2014 IEEE.

关键词： Federated learning

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1+1>2: A Dual-Function Defense Framework for Adversarial Example Mitigation

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IEEE Transactions on Information Forensics and security 2025年 20卷 4121-4136页

作者： Yang, Rui Sun, Qindong Cao, Han Shen, Chao Cai, Jiaming Rong, Dongzhu Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China Xi’an University of Technology Shaanxi Key Laboratory of Network Computing and Security Xi’an710049 China Xi’an Jiaotong University School of Software Engineering Xi’an710049 China

Current state-of-the-art plug-and-play countermeasures for mitigating adversarial examples (i.e., purification and detection) exhibit several fatal limitations, impeding their deployment in safety-critical real-world applications. These limitations include susceptibility to adaptive attacks, adverse impact on benign samples, high time consumption for conducting a complete defense cycle, etc. To bridge the gap, developing more advanced plug-and-play countermeasures is urgently needed to safeguard these applications. Specifically, this paper first proposes a novel method named Gaussian-augmented GAN-based Adversarial Purification (GA-GAP). Unlike previous methods, GA-GAP enhances the density of the training data in low-robustness regions by using random Gaussian noise. Moreover, GA-GAP incorporates a pre-trained deep learning classifier into the training architecture and integrates its classification loss into the training loss function. Then, following the development of GA-GAP, this paper innovatively proposes a dual-function defense framework named Adversarial Detection on Purification (ADoP) to mitigate adversarial examples further. In ADoP, purification and detection complement each other, achieving the effect of 1+1 >2 , which can more efficiently avoid adaptive attacks. Extensive experiments on ImageNet demonstrate that ADoP outperforms other countermeasures in multiple aspects. These aspects include superior generalization capability in purifying and detecting various adversarial examples, less adverse impact on benign samples, and practical time consumption for conducting a complete defense cycle. © 2005-2012 IEEE.

关键词： Adversarial machine learning

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RF-Sauron:Enabling Contact-Free Interaction on Eyeglass Using Conformal RFID Tag

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IEEE Internet of Things Journal 2025年第10期12卷 14320-14332页

作者： Yang, Baizhou Chen, Ling Peng, Xiaopeng Chen, Jiashen Tang, Yani Wang, Wei Fang, Dingyi Feng, Chao Northwest University Shaanxi Key Laboratory of Passive Internet of Things and Neural Computing School of Information Science and Technology Xi'an710127 China Northwest University Xi'an Key Laboratory of Advanced Computing and Software Security School of Information Science and Technology Xi'an710127 China Rochester Institute of Technology RochesterNY14623 United States Northwest University Xi'an Key Laboratory of Advanced Computing and System Security School of Information Science and Technology Xi'an710127 China Northwest University Shaanxi International Joint Research Centre for the Battery-Free Internet of Things School of Information Science and Technology Xi'an710127 China

Smart eyeglasses are emerging as a new medium for human-computer interaction. Existing solutions typically rely on cameras or touchpads, raising privacy invasion concerns or requiring users to physically interact with the glass frames. Here, we present, a novel mid-air gesture interaction system for eyeglasses, based on radio frequency identification (RFID). The design of our system involves embedding a conformal RFID tag into the eyeglass frame, where the received signals change with user gestures. We optimize the radiation gain of the tag to avoid view blockage while preserving a long working range. To discriminate different gestures, we propose an adaptively weighted multi-channel fusion network to extract their respective distinctive features. To allow efficient adaptation of the pre-trained network to new users, we also introduce a novel diagonal dot-product attention in our contrastive learning framework to uncover the feature similarities of different users. The proposed system was evaluated through extensive experiments, demonstrating an average recognition accuracy of 98.86% across twenty users and a cross-user accuracy of 98.75%. © 2014 IEEE.

关键词： Contrastive Learning

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Underwater target recognition based on adaptive multi-feature fusion network

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Multimedia Tools and Applications 2025年第10期84卷 7297-7317页

作者： Pan, Xiaoying Sun, Jia Feng, TianHao Lei, MingZhu Wang, Hao Zhang, WuXia Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an710121 China School of Computer Science and Technology Xi’an University of Post & Telecommunications Xi’an710121 China

Due to the complexity of the underwater environment, underwater acoustic target recognition is more challenging than ordinary target recognition, and has become a hot topic in the field of underwater acoustics research. In recent years, deep learning has been widely used in underwater recognition due to its powerful feature learning capabilities. However, most deep learning-based underwater target recognition methods use only the time-frequency domain features of signals, ignoring the frequency domain image features of signals. Additionally, they only use simple summation or splicing strategies to fuse and discriminate the extracted features, which leads to limited improvement in the accuracy of underwater target recognition. To address these issues, this paper proposes an underwater target recognition method based on an adaptive multi-feature fusion network. The method consists of a data pre-processing module, a multi-dimensional feature extraction module, and an adaptive multi-feature fusion module. In the multi-dimensional feature extraction module, the long short time memory network (LSTM) and the one-dimensional convolutional neural network (1DCNN) are used to extract the time-frequency features of the underwater signal. Furthermore, the two-dimensional convolutional neural network (2DCNN) is used to extract the image features in the frequency domain. The adaptive multi-feature fusion module uses an attention mechanism for adaptive weighted fusion to make full use of the learned features. The effectiveness of the proposed method is validated on the ShipEar dataset, and the recognition accuracy reaches 94.92%, which is higher than other existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Method for Removing Reflections from Water Surface Images Based on Pre-trained Image Restoration

A Method for Removing Reflections from Water Surface Images ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Minghua Zhao Rui Zhi Shuangli Du Jing Hu Cheng Shi Lin Wang Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Reflections on the water surface hinder the extraction of valuable information from water surface images. To remove reflections from water surface images, we construct a synthetic dataset and propose a multi-task network for water surface reflection detection and removal. Specifically, we first use a U-Net-based reflection detection module to generate a reflection mask, followed by a GAN-based network to remove the reflection. To extract multi-level features from the images, we design a color feature extraction network and a detail feature extraction network. Finally, to enhance the model's ability to remove large-area reflections, we pre-train the reflection removal network on an image restoration dataset. Experimental results on the proposed synthetic dataset and real water surface reflection images from the Internet show that our method significantly outperforms other methods in water surface reflection detection and removal.

关键词： Image color analysis Signal processing Feature extraction Multitasking Reflection Image restoration Speech processing Surface treatment Synthetic data Sports

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