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Lip-Audio Modality Fusion for Deep Forgery Video Detection

Computers, Materials & Continua 2025年第2期82卷 3499-3515页

作者： Yong Liu Zhiyu Wang Shouling Ji Daofu Gong Lanxin Cheng Ruosi Cheng College of Cyberspace Security Information Engineering UniversityZhengzhou450001China Research Institute of Intelligent Networks Zhejiang LabHangzhou311121China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China Key Laboratory of Cyberspace Security Ministry of EducationZhengzhou450001China

In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.

关键词： Deep forgery video detection lip-audio modality fusion mel frequency cepstrum coefficient siamese neural network band-pass filter

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Design and Implementation of Routing Algorithms for Mobile Ad Hoc network Applying TDMA Strategy Based on Smart Antenna

Design and Implementation of Routing Algorithms for Mobile A...

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2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Li, Fuqiang Yan, Zeyu Xiao, Haitao Mu, Pengcheng Yuan, Yongqiong Zhang, Weile 20th Institute Key Laboratory of Technology on Datalink Xi'an China Xi'an Jiaotong University Moe Key Lab for Intelligent Networks and Network Security Xi'an China

ISBN: (纸本)9798350318012

This paper presents a routing algorithm for TDMA mobile ad hoc networks based on smart antennas. In this algorithm, a new proactive link state routing algorithm with QOS guarantee is proposed. The algorithm uses a new metric to determine the optimal path. Added assessments of the physical layer link quality and node load. This article also introduces simulation and experiments, including eight-node multi-hop simulation and three-node linear hopping experiments to show that our design choices are indeed quite effective. © 2023 IEEE.

关键词： Machine design

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High-precision end-to-end adaptive optics aided orbital angular momentum wireless communication with XFTC-Net

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Optics Letters 2025年第10期50卷 3265-3268页

作者： Xu, Panpan Chang, Huan Wang, Fei Gao, Ran Guo, Dong Zhu, Lei Li, Zhipei Zhou, Sitong Xu, Qi Chen, Jing Xin, Xiangjun Beijing100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Beijing100876 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology 250353 China

In this Letter, we demonstrate high-precision end-to-end adaptive optics (AO) technique based on the X-Shape Fusion Transformer-convolutional neural network (XFTC-Net) without an additional probe path to compensate for distortions caused by atmospheric turbulence (AT) in orbital angular momentum (OAM) optical wireless communication links. The experimental results show that the XFTC-Net effectively extracts the intensity distribution features of distorted OAM beams, predicting AT phase screens and identifying OAM modes. After XFTC-Net-aided AO compensation, the bit error rate of the OAM optical communication system remains below 3.8 × 10−3, staying within Cn2 = 1 × 10−15–1 × 10−13 m−2/3. Moreover, the optical power with l= 3 increases from 5.08 dBm to 8.23 dBm at a transmission distance of 800 m. These experimental results indicate that the proposed XFTC-Net AO technique effectively compensates for OAM beam distortion in both single- and multiplexed-channel OAM links, which effectively balances the system complexity and transmission performance of OAM-based free space optical (FSO) communication systems, opening up new avenues for enhancing the performances of high-capacity optical communication. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Fiber to the x

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Fusion of Time-Frequency Features in Contrastive Learning for Shipboard Wind Speed Correction

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Journal of Ocean University of China 2025年第2期24卷 377-386页

作者： SONG Jian HUANG Meng LI Xiang ZHANG Zhenqiang WANG Chunxiao ZHAO Zhigang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250000China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China

Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe ***,vessel motion and challenging environmental conditions often affect measurement *** address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind *** integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed *** using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed *** proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.

关键词： time series prediction wind speed correction comparative learning shipborne sensor

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Artificial Immune Detection for network Intrusion Data Based on Quantitative Matching Method

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Computers, Materials & Continua 2024年第2期78卷 2361-2389页

作者： CaiMing Liu Yan Zhang Zhihui Hu Chunming Xie School of Electronic Information and Artificial Intelligence Leshan Normal UniversityLeshan614000China Intelligent Network Security Detection and Evaluation Laboratory Leshan Normal UniversityLeshan614000China Internet Natural Language Intelligent Processing Key Laboratory of Education Department of Sichuan Province Leshan Normal UniversityLeshan614000China

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection *** paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching *** proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune ***,to improve the accuracy of similarity calculation,a quantitative matching method is *** model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown *** proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional *** experiment results show that the proposed model can detect intrusions *** has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

关键词： Immune detection network intrusion network data signature detection quantitative matching method

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An Adaptive Residual Coordinate Attention-Based network for Hat and Mask Wearing Detection in Kitchen Environments

An Adaptive Residual Coordinate Attention-Based Network for ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Ying Wu, Xiaoming Liu, Xiangzhi Chen, Hao Qi, Bei Dong, Huomin Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, mAP@0.5 has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models. © 2024 IEEE.

关键词： Lighting

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Integrating Structural Semantic Knowledge for Enhanced Information Extraction Pre-training

Integrating Structural Semantic Knowledge for Enhanced Infor...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yi, Xiaoyang Bao, Yuru Zhang, Jian Qin, Yifang Lin, Faxin College of Cyber Science Nankai University China College of Computer Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education China

ISBN: (纸本)9798891761643

Information Extraction (IE), aiming to extract structured information from unstructured natural language texts, can significantly benefit from pre-trained language models. However, existing pre-training methods solely focus on exploiting the textual knowledge, relying extensively on annotated large-scale datasets, which is labor-intensive and thus limits the scalability and versatility of the resulting models. To address these issues, we propose SKIE, a novel pre-training framework tailored for IE that integrates structural semantic knowledge via contrastive learning, effectively alleviating the annotation burden. Specifically, SKIE utilizes Abstract Meaning Representation (AMR) as a low-cost supervision source to boost model performance without human intervention. By enhancing the topology of AMR graphs, SKIE derives high-quality cohesive subgraphs as additional training samples, providing diverse multi-level structural semantic knowledge. Furthermore, SKIE refines the graph encoder to better capture cohesive information and edge relation information, thereby improving the pre-training efficacy. Extensive experimental results demonstrate that SKIE outperforms state-of-the-art baselines across multiple IE tasks and showcases exceptional performance in few-shot and zero-shot settings. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Revisit to Graph Neighborhood Cardinality Estimation

A Revisit to Graph Neighborhood Cardinality Estimation

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International Conference on Data Engineering

作者： Pinghui Wang Yuanming Zhang Kuankuan Cheng Junzhou Zhao MOE Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph data are ubiquitous in real-world systems such as social networks and protein-protein interaction networks. In many applications, nodes usually are associated with real-value attributes, e.g., age, income, and wealth. Recently, industry and research communities have attracted attention to mining and learning attribute graphs. In this paper, we study the problem of calculating the general neighborhood cardinality of each node $v$ in the graph, i.e., the sum of non-negative attribute values of the nodes in the $k$ -hop neighborhood of a node $v$ . The naive solution is to run a $k$ -step breadth-first-search (BFS) algorithm starting from each node and storing all visited nodes' attributes. Clearly, the time complexity of this solution is $O\left(\vert V\vert d_{\max }^k\right)$ , where $\vert V\vert$ is the number of nodes and $d_{\max}$ is the maximum node degree in the graph. In real-world networks such as Twitter, $d_{\max}$ is over $3\timesnetworks0^{6}$ . Therefore, it is infeasible to compute the neighborhood cardinality of nodes exactly in such massive networks even if we set $k=2$ . To solve this problem, we propose efficient methods to compute the neighborhood cardinality of graphs with non-negative node attributes and binary node attributes, respectively. Extensive experiments on large real-world networks show the efficiency and effectiveness of our methods.

关键词： Proteins Industries Accuracy Social networking (online) Blogs Estimation Data engineering

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BTP-CAResNet: An Encrypted Traffic Classification Method Based on Byte Transfer Probability and Coordinate Attention Mechanism

BTP-CAResNet: An Encrypted Traffic Classification Method Bas...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Junhao Shi, Huiling Zhang, Wei Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

With the extensive application of network traffic encryption technology, the accurate and efficient classification of encrypted traffic has become a critical need for network management. Deep learning has become the predominant method for traffic classification, primarily involving the transformation of network traffic into grayscale images and their subsequent classification using Convolutional Neural networks (CNNs). However, traditional grayscale image generation methods are plagued with issues of redundant and lost information, and conventional channel attention mechanisms are still insufficient in capturing key traffic features, collectively hindering the enhancement of classification performance. To tackle these issues, this paper introduces a classification method based on Byte Transfer Probability and Coordinate Attention Mechanism in Residual network (BTP-CAResNet). This method, on the foundation of the classic ResNet architecture, incorporates a new grayscale image generation method that utilizes Byte Transfer Probability, effectively overcoming the deficiencies of traditional approaches. Additionally, this paper integrates a Coordinate Attention Mechanism into the ResNet model, which effectively overcomes the limitations of traditional channel attention mechanisms and further improves the performance of traffic classification. Experimental validation on the ISCX VPN-nonVPN dataset demonstrates that, compared to previous CNN-based methods, the method proposed in this paper exhibits superior performance in key metrics such as accuracy, precision, recall, and F1 score. It provides a new perspective for traffic classification based on convolutional neural networks. © 2024 IEEE.

关键词： Convolutional neural networks

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CB-YOLO: A Small Object Detection Algorithm for Industrial Scenarios

CB-YOLO: A Small Object Detection Algorithm for Industrial S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Hao Wu, Xiaoming Su, Zhanzhi Zhao, Ying Dong, Yunfeng Liu, Xiangzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

In certain specific industrial scenarios, smoking and cellphone usage are strictly prohibited behaviors. In these scenarios, it is crucial to rapidly and accurately detect smoking and cell phone usage, and promptly issue warnings, to ensure industrial safety. The detection of prohibited behaviors using computer vision has gained attention from researchers with the development of artificial intelligence. However, detecting small objects like cigarettes and cell phones in complex backgrounds and varying angles poses a challenge due to their changing shapes. In this paper, we propose a detection model called CB-YOLO, which utilizes YOLOv7 as the baseline model, for detecting smoking and mobile phone usage behavior. Firstly, we propose a new channel space pyramid network called CSPPF that pools features at different scales and introduces scale focusing on improving the perceptual ability of the network to better handle targets at various scales and locations. Secondly, we propose an enhanced feature pyramid called AWBFPN. This introduces additional learnable weight parameters to improve the model's ability to fuse multi-scale features effectively. Finally, we have also proposed a new loss function called Size-IoU. The experimental results demonstrate that our algorithm outperforms the baseline model. Specifically, it achieves a 3.9% improvement in Precision, a 2.1% improvement in Recall, a 6.4% improvement in mAP@0.5, and a 2% improvement in mAP@0.5:0.95 on the Phone_check dataset. Similarly, on the Smoking dataset, our algorithm achieves a 3.3% improvement in Precision, a 1.9% improvement in Recall, a 1.7% improvement in mAP@0.5, and a 0.3% improvement in mAP@0.5:0.95. © 2024 IEEE.

关键词： Object detection

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