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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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Vertical Federated Learning Based on Consortium Blockchain for data Sharing in Mobile Edge Computing

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Computer Modeling in Engineering & sciences 2023年第10期137卷 345-361页

作者： Yonghao Zhang Yongtang Wu Tao Li Hui Zhou Yuling Chen State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyWeifang262700China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin541004China

The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the ***,certain data is quite sensitive,and sharing it directly may violate *** Federated Learning(VFL)is a secure distributed machine learning framework that completes joint model training by passing encryptedmodel parameters rather than raw data,so there is no data privacy leakage during the training ***,the VFL can build a bridge between data demander and owner to realize data sharing while protecting data ***,the VFL requires a third party for key distribution and decryption of training *** this article,we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in *** specifically,we propose a V-Raft consensus algorithm based on Verifiable Random Functions(VRFs),which is a variant of the *** VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by ***,we apply secret sharing todistribute the private key to avoid the situationwhere the training result cannot be decrypted if the leader ***,we analyzed the performance of the V-Raft and carried out simulation experiments,and the results show that compared with Raft,the V-Raft has higher efficiency and better scalability.

关键词： Mobile edge computing vertical federated learning consortium blockchain consensus algorithm

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Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

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Cross-Chain Asset Trading Scheme Without Trusted Third-Party 2nd

Cross-Chain Asset Trading Scheme Without Trusted Third-Party

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2nd International Conference on data Security and Privacy Protection, DSPP 2024

作者： Li, Bo Chen, Yuling Huang, Siyuan Yang, Yuxiang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9789819785391

The development of blockchain technology is influenced by user diversity and application scenarios, resulting in limitations on the applicability of assets. Although cloud computing is renowned for facilitating cross-chain token transfers, it lacks a robust trust mechanism. Existing cross-chain transaction solutions are typically confined to interactions between two blockchains or within established networks. Hence, constructing a trustless cross-chain transaction protocol remains a significant challenge. This study proposes a trustless third-party cross-chain asset trading solution, Vchain, based on a Blockchain of Blockchains architecture and driven by a relay chain. This solution supports various blockchain ecosystem accounts and leverages cloud computing to integrate external data sources and API calls to obtain the latest token exchange rates, ensuring transaction accuracy and real-time performance. Simulation results demonstrate that Vchain’s cross-chain transaction costs are 69.25% lower than those of existing models. The research indicates that Vchain offers significant advantages in enhancing transaction efficiency and reducing costs, effectively addressing current challenges in cross-chain transactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Asset transactions Cross-chain Relay chain

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TCRosetta:An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences

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Genomics, Proteomics & Bioinformatics 2024年第4期22卷 181-190页

作者： Tao Yue Si-Yi Chen Wen-Kang Shen Zhan-Ye Zhang Liming Cheng An-Yuan Guo Center for Artificial Intelligence Biology Hubei Bioinformatics&Molecular Imaging Key LaboratoryCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Laboratory Medicine Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Thoracic Surgery West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China

T cells and T-cell receptors(TCRs)are essential components of the adaptive immune *** of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and *** TCR repertoire studies have attracted much attention,there are few online servers available for TCR repertoire analysis,especially for TCR sequence annotation or advanced ***,we developed TCRosetta,a comprehensive online server that integrates analytical methods for TCR repertoire analysis and *** combines general feature analysis,large-scale sequence clustering,network construction,peptide–TCR binding prediction,generation probability calculation,and k-mer motif analysis for TCR sequences,making TCR data analysis as simple as *** TCRosetta server accepts multiple input data formats and can analyze�20,000 TCR sequences in less than 3 *** is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://***/TCRosetta/.

关键词： T-cell receptor TCR repertoire analysis Batch search Annotation Web server.

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Structural insights, synthesis, and electrocatalysis of high entropy nanoparticles for fuel cell, metal-air battery, and water-splitting applications

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Progress in Natural science:Materials International 2024年第3期34卷 512-523页

作者： Xin Tong Hao Ye Yunrou Wu Xinxing Zhan Manqi Gu Shixia Luo Jiangning Gong Juan Tiana Yadian Xie School of Chemistry and Material Science Guizhou Normal University Center for the Physics of Low-Dimensional Materials School of Physics and Electronics School of Future Technology Henan University Key Laboratory of Low-Dimensional Materials and Big Data Guizhou Minzu University

High-entropy alloy nanoparticles(HEA-NPs) have recently sparked great interest in materials science. Their solidsolution states, derived from distinct HEA configurations, make them promising candidates for catalysts with exceptional activity, stability, and tunable performance. However, a comprehensive understanding of the underlying mechanisms governing their electrocatalytic behavior is still lacking, hindering the rational design of HEA electrocatalysts. This review summarizes the fundamental knowledge of HEA-NPs, including the structureactivity correlations of HEA-NPs, diverse synthesis strategies, and applications in electrochemical catalysis. The design strategies for guiding improvements in tunable performance were highlighted. The article concludes with insights, perspectives, and future directions, encapsulating the state-of-the-art knowledge and paving the way for further exploration in this dynamic field.

关键词： High-entropy alloy nanoparticles Structure-activity correlations Synthesis strategies Electrochemical catalysis

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A Transformer-Based Video Colorization Method Fusing Local Self-attention and Bidirectional Optical Flow 10th

A Transformer-Based Video Colorization Method Fusing Local S...

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10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Yuan, Lin Duan, Xun Xiang, Lingjie Kong, Guangqian Wu, Yun State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819787456

Video colorization encounters two principal challenges: colorization quality and temporal flicker. Balancing colorization quality and temporal consistency is a significant challenge. To address the aforementioned issues, we employ the transformer model in the field of video colorization and introduce a pioneering video colorization method named VCTR. Initially, to guarantee the quality of video coloring, we employ a pretrained image coloring network to add color to the grayscale video frame. Next, the feature extraction module for VCTR is utilized to perform feature extraction and propagation on the color frames during the colorization process. Finally, the transformer module is designed to fully leverage local information via the local feature self-attention layer. Additionally, motion information from bidirectional optical flow is utilized to identify correlations across video frames for feature fusion, guaranteeing both the coloring effect and temporal consistency. The experimental results demonstrate that VCTR outperforms existing methods on two publicly available datasets, namely, DAVIS and Videvo. VCTR attains the top ranking in the long-series dataset, Videvo, based on the CTBI (Colorization and Temporal-Consistency Balance Index). This achievement underscores VCTR’s ability to strike a commendable balance between colorization quality and temporal consistency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical flows

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Traceable Ring Signature Scheme in Internet of Vehicles Based on Lattice Cryptography 21

Traceable Ring Signature Scheme in Internet of Vehicles Base...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liao, Wei Han, Lansheng Chen, Peng Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users' public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners. © 2023 IEEE.

关键词： Cryptography

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Sparse representation scheme with enhanced medium pixel intensity for face recognition

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CAAI Transactions on Intelligence technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test *** has been widely used in various image classification *** in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for *** deformable images such as human faces,pixels at the same location of different images of the same subject usually have different ***,extracting features and correctly classifying such deformable objects is very ***,the lighting,attitude and occlusion cause more *** the problems and challenges listed above,a novel image representation and classification algorithm is ***,the authors’algorithm generates virtual samples by a non-linear variation *** method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable *** combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the ***,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion *** weighting coefficients in the score fusion scheme are set entirely ***,the algorithm classifies the samples based on the final *** experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

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RUP2S- YOLO: An Improved YOLOv8-Based Algorithm for Dense Pedestrian Detection 5

RUP2S- YOLO: An Improved YOLOv8-Based Algorithm for Dense Pe...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Wang, Juanmin Huang, Kun Pi, Jianyong College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guizhou Guiyang China

ISBN: (纸本)9798350385557

Dense pedestrian detection is a key research direction in the field of computer vision, which plays a significant role in large-scale crowded public spaces. It provides strong technical support for security assurance, traffic management, and behavioral analysis in public areas. To address the issue of missed detection of small-sized occluded pedestrians in dense scenarios, this paper proposes an improved detection algorithm based on YOLOv8, named RUP2S-YOLO. For the backbone network, the RFCBAM module is used to replace the convolution module to enhance the capability of capturing subtle features and better extract the characteristics of small-scale pedestrians;for the C2f module, the UKMS module is designed as a substitution aiming to improve the model's capacity to process information of different scales;for the detection head, the P2S module is introduced for the accurate positioning of small-scale targets. Experiments on dense pedestrian datasets show that the improved RUP2S-YOLO outperforms the original algorithm in terms of performance, achieving an AP50 of 82.8%, and an AP50:95 of 55.8%, which represents an increase of 2.2% and 2.8% respectively compared to the original algorithm. Meanwhile, in comparison with other leading pedestrian detection models, the improved YOLOv8 algorithm presented in this article also demonstrates competitive advantages. © 2024 IEEE.

关键词： Object detection

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