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Conditional selection with CNN augmented transformer for multimodal affective analysis

CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Near-Lossless Compression of Two Independent Binary Sources with One Encoder 16

Near-Lossless Compression of Two Independent Binary Sources ...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Zhu, Tingting Ma, Xiao Sun Yat-sen University School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9798350390643

In this paper, we consider the near-lossless compression of two distinct but independent binary random sources U and V, both of which are asymmetrical independent and identically distributed (i.i.d.) sources. In particular, V is assumed to be sparser than U. We show that, to efficiently compress U and V, explicit source coding (compression) for V (the sparser one) is not required. The basic idea is to encode U with an error-correction code and then superimpose V on the codeword. Hence, only one encoder-decoder pair (EDP) suffices to compress two sources. Theoretical analysis shows that the proposed coding scheme is entropy-approaching in terms of bit-error rate (BER) of both sources, and numerical results verify our analysis. Furthermore, we analyze the complexity of compression using the block Markov superposition transmission-repetition (BMST-R) codes to discuss the advantage of the proposed scheme. Simulation results demonstrate that the proposed scheme compresses the two sources simultaneously in a more compact way but causes no performance degradation. © 2024 IEEE.

关键词： Convolutional codes

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Phishing detection on Ethereum via transaction subgraphs embedding

IET Blockchain

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IET Blockchain 2023年第4期3卷 194-203页

作者： Lv, Haifeng Ding, Yong Guangxi Key Laboratory of Machine Vision and Intelligent Control WuZhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Industry Software Technology WuZhou University Wuzhou China

With the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph-based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Graphic methods

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SSpose: Self-Supervised Spatial-Aware Model for Human Pose Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5403-5417页

作者： Yu, Linfang Qin, Zhen Xu, Liqun Qin, Zhiguang Choo, Kim-Kwang Raymond University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Industrial Research Institute Chengdu610213 China University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu610054 China Industrial Research Institute Chengdu610213 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human pose estimation (HPE) relies on the anatomical relationships among different body parts to locate keypoints. Despite the significant progress achieved by convolutional neural networks (CNN)-based models in HPE, they typically fail to explicitly learn the global dependencies among various body parts. To overcome this limitation, we propose a spatial-aware HPE model called SSpose that explicitly captures the spatial dependencies between specific key points and different locations in an image. The proposed SSpose model adopts a hybrid CNN-Transformer encoder to simultaneously capture local features and global dependencies. To better preserve image details, a multiscale fusion module is introduced to integrate coarse- and fine-grained image information. By establishing a connection with the activation maximization (AM) principle, the final attention layer of the Transformer aggregates contributions (i.e., attention scores) from all image positions and forms the maximum position in the heatmap, thereby achieving keypoint localization in the head structure. Additionally, to address the issue of visible information leakage in convolutional reconstruction, we have devised a self-supervised training framework for the SSpose model. This framework incorporates mask autoencoder (MAE) technology into SSpose models by utilizing masked convolution and hierarchical masking strategy, thereby facilitating efficient self-supervised learning. Extensive experiments demonstrate that SSpose performs exceptionally well in the pose estimation task. On the COCO val set, it achieves an AP and AR of 77.3% and 82.1%, respectively, while on the COCO test-dev set, the AP and AR are 76.4% and 81.5%. Moreover, the model exhibits strong generalization capabilities on MPII. © 2024 IEEE.

关键词： Supervised learning

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Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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Tsinghua Science and Technology 2024年第2期29卷 305-324页

作者： Yiqiao Cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain Technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of Computer Sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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DBKEM-AACS: a distributed key escrow model in blockchain with anonymous authentication and committee selection

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Science China(information Sciences) 2023年第3期66卷 289-290页

作者： Axin XIANG Hongfeng GAO Youliang TIAN Liang WAN College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University Network and Information Management Center Guizhou University State Key Laboratory of Public Big Data Guizhou University Institute of Computer Software and Theory Guizhou University

Dear editor, key security is of great practical significance and demand to guarantee the security of digital assets in the blockchain system. At present, users prefer to escrow their assets on centralized institutions, but this phenomenon has gone against the unique characteristics of decentralization and anonymity in the blockchain. Among them, the suspense incidents of assets lock or lost are enough to prove that the security of escrowed keys on the exchanges is questionable.

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A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

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computers, Materials & Continua 2024年第8期80卷 2161-2179页

作者： Wen Jiang Mingshu Zhang Xu’an Wang Wei Bin Xiong Zhang Kelan Ren Facheng Yan College of Cryptography Engineering Engineering University of People’s Armed PoliceXi’an710086China Key Laboratory of Network and Information Security Engineering University of People’s Armed PoliceXi’an710086China Key Laboratory of CTC&Information Engineering Engineering University of People’s Armed PoliceMinistry of EducationXi’an710086China

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more *** detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific *** order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is *** method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature *** algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news ***-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the *** then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that ***,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for *** experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

关键词： Fake news detection domain-related emotional features semantic features feature fusion

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Lip-Audio Modality Fusion for Deep Forgery Video Detection

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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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IoV Edge Data Integrity Audit Method Based on Blockchain 3

IoV Edge Data Integrity Audit Method Based on Blockchain

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3rd International Conference on Blockchain Technology and information security, ICBCTIS 2023

作者： Qi, Hui Li, Hang Yu, Wangdong Wang, Chunbo Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun China Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology School of Information Technology Changchun Automobile Industry Institute Changchun China

ISBN: (纸本)9798350340402

With the quick development of Internet of Vehicles (IoV) technology, a lot of private data of vehicle users has been generated. An increasing amount of vehicle data is being collected in the cloud storage providers (CSP) and the edge node (EN). These data involve user privacy and vehicle driving data, and once they are damaged or lost, they will lead to serious traffic accidents. Therefore, safeguarding the security and integrity of IoV data has become a hot issue. However, due to the distributed nature of the IoV data system and the difficulty of edge node management, the traditional data integrity auditing methods can no longer satisfy the needs in the IoV cloud-edge collaboration scenario. Therefore, we propose a IoV edge data integrity audit method based on blockchain First, our design a novel cloud-edge collaborative storage model that not only allows both cloud and edge to participate in the audit, but also enables the data user (DU) to quickly access the IoV data and reduce the pressure of data transmission. Second, during the audit phase, we use the third-party auditor (TPA) to design a low-overhead audit algorithm and ensure that the privacy of the IoV data is not compromised. Finally, the TPA records audit transactions on the blockchain, so that the DU can re-review the TPA's audit results to find out whether the TPA is delaying and falsifying the audit result. Theoretical analysis shows that our method can protect the privacy of IoV data. Meanwhile, the experimental results show that our method has a low time overhead and the storage model is suitable for the IoV environment. © 2023 IEEE.

关键词： Blockchain

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