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IEEE information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yanxia Liang Tong Jia Xin Liu Huanhuan Zhang Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China College of Information Engineering Xi’an Eurasia University Xi’an China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The flexibility and maneuverability of unmanned aerial vehicles (UAVs) have led to their widespread use, but the challenge of limited storage space and bandwidth in emergency scenarios necessitates efficient image compression techniques. In this research, we propose an image compression algorithm tailored for UAV communication, termed ROIE-UC (Region of Interest Extraction for UAV Communication). Firstly, image pixels are clustered into super pixel blocks using the Simple Linear Iterative Clustering (SLIC). Secondly, these super pixels are grouped into regions of interest (ROI) using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The image is then segmented into ROI and non-ROI areas based on these clusters. Lossless compression is applied to the ROI, while lossy compression with a high ratio is used for non-ROI regions. At the receiving end, the image is decompressed and reconstructed. Experimental results show that the ROIE-UC achieves a Peak Signal-to-Noise Ratio (PSNR) of 46.37 dB and a Structural Similarity Index (SSIM) of 99.94% for the ROI part, with compression ratios of 27, 25, and 14 for the whole image. Compared to the JPEG, PSNR improved by up to 28.52% and SSIM by 1.51%, with a compression ratio increase from 14.52 to 27.56. The ROIE-UC also provides up to a 49.44% increase in PSNR over other methods. These results demonstrate that ROIE-UC is an efficient and effective solution for image processing and data compression in UAV applications.

关键词： Image segmentation Visualization Image coding PSNR Wireless networks Bandwidth Autonomous aerial vehicles Real-time systems Image reconstruction Videos

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APFT: Adaptive Phoneme Filter Template to Generate Anti-Compression Speech Adversarial Example in Real-Time

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IEEE Transactions on information Forensics and security 2025年 20卷 5152-5165页

作者： Huang, Yihuan Ren, Yanzhen Sun, Zongkun Zhai, Liming Wang, Jingmin Liu, Wuyang Wuhan University School of Cyber Science and Engineering Wuhan430072 China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Central China Normal University School of Computer Science Wuhan430079 China

Automatic Speech Recognition (ASR) systems are widely used for speech censoring. Speech Adversarial Example (AE) offers a novel approach to protect speech privacy by forcing ASR to mistranscribe. However, existing speech AE faces two challenges in real-time voice communication scenarios, such as IP telephone, voice chat, or video conference, it cannot be generated in real-time, and its defensive capability is significantly reduced after the essential audio compression for network transmission. In this paper, we propose Adaptive Phoneme Filter Template (APFT) to address these issues. The key features of APFT include: 1) Phoneme-level Templates for universal AE generation in real-time, 2) Filter, which eliminates redundant signals to improve compression robustness. 3) Adaptive Band Filtering, which limits the attack area from the frequency band without affecting the attack effectiveness and improves speech quality. The comprehensive experimental results show that APFT has four advantages: 1) Real-time Generation, with AE generation time below 1.1ms for 1s speech;2) Compression Robustness, achieving a WER of 0.64 under AAC and Opus codecs;3) Transferability, with an average WER of 0.72 across datasets and ASR systems;4) Stealthiness, achieving a MOS of 4.07 for high-quality speech. In addition, the experiment on Telegram voice calls further proves the practical applicability of APFT. © 2005-2012 IEEE.

关键词： Speech enhancement

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Crucial Interaction Elements Detection for 5G Core network based on the Complex network Model

Crucial Interaction Elements Detection for 5G Core Network b...

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International Conference on networking and network Applications (NaNA)

作者： Ruinan Peng Xinhong Hei Yichuan Wang Xiaoxue Liu Yeqiu Xiao School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

This paper focuses on the issue of crucial interaction elements detection in the 5G core network. Given the current absence of an effective method for quantification evaluating the importance of interaction elements, we define the crucial interaction elements as crucial nodes in this paper, and also undertake a comprehensive exploration of various data interaction scenarios within the 5G core network. This aims to overcome the limitations of traditional network analysis methods, which often operate independently in multi-layer data processing. By extracting multilayer data interaction elements across diverse scenarios, these elements are characterized within a complex network framework. Subsequently, a quantification scoring system for crucial nodes is constructed, incorporating multiple centrality indicators and node weights. Notably, the fitness function within the genetic algorithm is improved to facilitate a dynamic weight optimization strategy, responding to real-time changes in network topology. The optimized weight combination is then allocated to various evaluation indicators within the system, culminating in the design of a quantification scoring model for crucial node. Based on this model, a suite of 5G core network crucial point detection methods, tailored to different data scenarios is proposed. This method enable the quantification evaluation of each node’s importance, realizes crucial interaction elements detection. Such an approach serves to bolster the stable operation of the 5G core network, thereby more effectively meeting modern society’s security imperatives concerning information transmission.

关键词： 5G mobile communication Transfer learning Complex networks Reinforcement learning Nonhomogeneous media Data models Robustness

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Region Partition based Hybrid Deep network for Polarimetric SAR Image Classification

Region Partition based Hybrid Deep Network for Polarimetric ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junfei Shi Linjing Xu Haiyan Jin Wei Wang Rong Fei Shanshan Ji Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The Convolutional Neural network (CNN) model excels at learning local features, but struggles with capturing global large-scale features, particularly in extremely heterogeneous areas. In contrast, the Graph Convolution network (GCN) is an effective tool for PolSAR image classification, demonstrating a capability to learn large-scale global features *** learn effective features for both heterogenous terrain objects and edge details well, a novel region partition based hybrid deep network is proposed for adaptive learning features for boundary and non-boundary regions, which can learn both large-scale global features for extremely heterogeneous terrain objects and pixel-wise features for edge details. The proposed method can effectively partition a PolSAR image into boundary and non-boundary regions, and design a CNN and GCN subnetworks for them respectively. Subsequently, a unified network is designed to effectively fuse both the advantages of GCN and CNN to enhance classification performance. The experiments verify the proposed algorithm can achieve better performance than compared methods in both region homogeneity and boundary preservation.

关键词： Accuracy Image edge detection Semantics Neural networks Geoscience and remote sensing Radar polarimetry Classification algorithms

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A Novel Auralization Model for network security Situation Assessment

A Novel Auralization Model for Network Security Situation As...

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International Conference on networking and network Applications (NaNA)

作者： Yeqiu Xiao Yibin Ma Yichuan Wang Kun Jiang Lanlan Cui School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Situation assessment is one of indispensable part for protecting network security. However, with the development of network attacks, the timeliness of attack threat assessment using traditional mathematical methods has gradually become difficult to apply to large-scale and high-speed network attacks. In order to solve this problem, this paper explores the relationship between the influencing factors of network threats and music elements, studies a new method of situation assessment that combines network situation with MIDI music, and builds a network situation auralization model. First, for the network situation assessment model, this paper starts from the perspective of network attacks, designs and extracts attack attributes, and constructs a sliding window-based attack threat dynamic assessment strategy to evaluate risk levels and reflect the current network security status. Secondly, for the MIDI music mapping model, extract key attributes in music, design an emotion classification model based on MIDI music and build a MIDI music library, and finally map it to the network situation. Experimental results show that the auralization model in this paper is more efficient in situation assessment and presentation.

关键词： Deep learning Accuracy High-speed networks network security Threat assessment Libraries

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Hyperspectral Band Selection Based on Classification Reward Adjustment

Hyperspectral Band Selection Based on Classification Reward ...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Jing Hu Jiawei Liang Yunfei Ju Minghua Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Hyperspectral image band selection is a crucial link in image processing. It is necessary to screen out bands with rich information and low correlation, to achieve data dimensionality reduction and retain key information, thereby improving image processing efficiency. Aiming at the differences in reflectance characteristics of targets in different regions in hyperspectral images and the strong correlation between bands, this paper proposes a gradient-guided spatial-spectral weighted hyperspectral band selection method. Firstly, gradient information is introduced to enhance the edge texture features of different targets. Then, the entropy rate superpixel segmentation algorithm is used to segment the first principal component of the image and divide multiple uniform regions. Secondly, to more comprehensively capture the global relationship between all bands in the image, a multi-graph complementary strategy is proposed to construct a regional similarity graph, aiming to maximize the mutual contribution between superpixels with spatial-spectral similarity. Finally, a unified clustering graph is generated by spectral clustering. After normalized cutting, the band with the least noise information in each sub-cube is selected to form a new band subset. The effectiveness of the proposed method is verified by classification experimental results and data analysis.

关键词： Training Reflectivity Image segmentation Correlation Noise Memory modules Predictive models Smart systems Spatiotemporal phenomena Hyperspectral imaging

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Nonlinear Interference-Aware Routing,Wavelength and Power Allocation in C+L+S Multi-Band Optical networks

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China Communications 2025年第4期22卷 129-142页

作者： Zhang Xu Xie Wang Feng Chuan Zeng Hankun Zhou Shanshan Zhang Fan Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical *** overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength ***,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical *** objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not ***,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation ***,we conduct simulations under different test *** simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.

关键词： multiband optical communications multiband optical networks power allocation wavelength assignment

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A Novel Multipath Data Transmission Method with Traffic Obfuscation

A Novel Multipath Data Transmission Method with Traffic Obfu...

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International Conference on networking and network Applications (NaNA)

作者： Xinhong Hei Jianfeng Chen Yichuan Wang Xin Song Lei Zhu School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

With the rapid progress of network communication technology, the issues of user identity and privacy are critical. However, there are many disadvantages in traditional single-path data transmission, for example, the link is susceptible to monitoring and the traffic feature can be easily analyzed. To solve this issue, this paper introduces a multipath data transmission strategy with traffic obfuscation. First, we design a multipath traffic transmission scheme that divides traffic into multiple sub streams and transmits them through multiple paths. The ability of the communication system to resist surveillance and traffic analysis is enhanced by this approach. Second, we effectively confused multipath traffic by changing the size of the data packets, controlling the delay of multipath transmission, and adding redundant traffic. Through experiments, it has been shown that in this advanced multipath transmission strategy, there is no significant difference in the traffic features generated by each node. The effectiveness of the proposed method in obfuscating communication traffic is demonstrated by this.

关键词： Privacy Surveillance Resists Traffic control Delays Data communication Streams

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Compiler Differentiation Detection Method for Binary Similarity

Compiler Differentiation Detection Method for Binary Similar...

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International Conference on networking and network Applications (NaNA)

作者： Xiaoxue Liu Xinwei Duan Xinhong Hei Yichuan Wang Xin Song School of computer science and engineering Xi’an University of technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

Compiler differential detection can be performed by detecting the similarity between the binary compiled by the compiler and the ideal compiled file. This article proposes a javac compiler differentiation detection system based on binary file similarity: we extract opcode sequences from binary files, use BERT to extract features of the opcodes, and finally obtain the similarity score by calculating the distance between vectors. Under the premise of ensuring the retrieval speed requirements, this algorithm is superior to most existing models in accuracy, with the accuracy rate of $99.7 \%$. It shows that the method in this article can effectively detect whether two binary files are similar. This method will have a good effect on detecting whether the javac compiler has been invaded by malicious code in the future.

关键词： Accuracy Limiting Source coding Semantics Neural networks Feature extraction Malware

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

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

作者： Guangjin Wang Bao Wang Fuyong Xu Zhenfang Zhu Peipei Wang Ru Wang Peiyu Liu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model’s "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model’s ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method.

关键词： Semantics Benchmark testing Speech enhancement Signal processing Vectors Acoustics Context modeling

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