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arXiv 2024年

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2024, CC BY-NC-ND.

关键词： Semantics

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SiamTBD:A Novel Algorithm for Small and Dim Target Tracking Using Video SAR

SiamTBD:A Novel Algorithm for Small and Dim Target Tracking ...

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第43届中国控制会议

作者： Zhenyuan Li Jing Liu Jianting Zhang Haolong Wang Xinyuan Ji Shuncheng Tan Hailang Zhang Ministry of Education Key Laboratory for Intelligent Network and Network Security School of Automation Science and EngineeringFaculty of Electronic and Information Engineering Xi'an Jiaotong University No.91977 Unit of the Chinese People's Liberation Army Institute of Information Fusion Naval Aviation University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

A large number of publications on detection and tracking have appeared in the field of video synthetic aperture radar(Video SAR) due to its capability of forming high-resolution videos for a region of interest. However, Video SAR tracking faces many challenges, e.g., the dim and small target shadows with weak visual features, interference from nearby similar objects,etc. Moreover, the target may be submerged in strong clutters for a short period. In this work, a novel Siamese Region Proposal network(SiamRPN) based track-before-detect algorithm, namely SiamTBD, is proposed to address the above issues. The SiamRPN network is chosen as the tracking backbone, considering its superiority in speed and accuracy. Moreover, a multiframe tree composed of root/branch/leaf bounding boxes in different frames is constructed based on multi-frame information. In each frame, we select multiple templates based on the scores assigned to the branch/leaf bounding boxes, which are calculated based on both the appearance features and the motion information. Furthermore, inspired by the idea from the track-before-detect(TBD) technique, the information from multiple frames is accumulated and finally used to determine the existence of the target as well as estimate its trajectory. A number of experiments are conducted using the Video SAR data released by Sandia National laboratory. The experimental results show that the proposed SiamTBD algorithm outperforms several state-of-the-art correlation filter-based and Siamese network-based tracking algorithms.

关键词： Video synthetic aperture radar(Video SAR) Siamese Region Proposal network(SiamRPN) track-before-detect(TBD) multi-frame tree moving target tracking

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A Design of Direct Memory Access Controller for Wireless Communication SoC in Power Grid

A Design of Direct Memory Access Controller for Wireless Com...

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International Conference on Circuits, System and Simulation (ICCSS)

作者： Yingying Chi Zhe Zheng Research Department of Smart Chip Global Energy Interconnection Research Institute Co. Ltd. Beijing China State Grid Key Laboratory of Information & Network Security State Grid Laboratory of Electric Power Communication Network Technology Beijing China

ISBN: (纸本)9781538678206

This paper presents a complete Register Transfer Level (RTL) design of Direct Memory Access (DMA) controller which is compliable with the Advanced Highperformance Bus (AHB). The DMA is integrated in Cortex-M3-based system-on-chip (SoC) designed for power-grid-dedicated wireless communication system based on IEEE 802.11ah protocol. The hardware implementation and prototype verification indicate that by allowing hardware devices working at different frequencies to communicate with each other without relying on the heavy outage load of CPU, the proposed DMA controller realizes the good convergence with the whole system and improves the overall performance with low power consumption.

关键词： Registers Hardware Writing Wireless communication Data transfer System-on-chip

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An Algorithmic Aspect of Zernike Moments in the Presence of Rotation

An Algorithmic Aspect of Zernike Moments in the Presence of ...

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The First International Conference on Multimedia information networking and security(第一届国际多媒体网络信息安全会议 MINES 2009)

作者： Shijun Xiang Department of Electronic Engineering School of Information Science and Technology Jinan University Guangzhou 510632 China Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou 350007 China

Zernike moments are widely used in image processing fields (e.g., rotation-invariant watermarking) based on many desirable properties, such as rotational invariance, noise robust and efficient representation of pattern. In image processing operations, an image after rotation will result in a bigger size for keeping the content. This will cause the difference among Zernike moments of an image and its rotated versions. In this paper, we investigate such a problem in detail and introduce a solution by normalizing original and its rotated versions into the same size. Furthermore, we propose an algorithmic scheme of Zernike moments' computation. Both theoretical analysis and experimental testing show that the proposed approach can efficiently avoid the effect of the image size change under rotation.

关键词： Image watermarking Rotation Zernike moments

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RFID indoor localization based on relational aggregation

RFID indoor localization based on relational aggregation

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Jiali Zheng Tuanfa Qin Jieming Wu Li Wan Guangxi Key Laboratory of Multimedia Communications and Network Technology (Cultivating Base) Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing School of Computer Electronics and Information Guangxi University Nanning China School of Information and Electronic Engineering WuZhou University Wuzhou China

This paper proposes a relational aggregation algorithm based on Radio Frequency Identification (RFID) to achieve accurate indoor localization. The proposed algorithm is composed of three steps: (1) exploring the relationship between reader received power and distance information then estimating Euclid distance of signal strength; (2) employing k-Nearest Neighbour algorithm to aggregate the relationship between nearest reference tag and target tag; (3) optimizing relational aggregation operator to obtain the coordinate of target tag. Simulated experiments show that the proposed algorithm can reduce mean localization error effectively and improve the accuracy of indoor localization.

关键词： Radiofrequency identification Mathematical model Antenna arrays Mobile computing Wireless communication Multimedia communication Mobile communication

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Test-Time Training with Invariant Graph Learning for Out-of-Distribution Generalization

SSRN

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SSRN 2024年

作者： Pi, Li Li, Jiaxing Song, Lei Yang, Guanyu Chen, Yang Kong, Youyong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Jiangsu Province Nanjing China

Graph Neural networks (GNNs) frequently do not perform optimally on out-of-distribution (OOD) data. Previous research on graph OOD generalization suggests extracting invariant subgraphs, which provide crucial categorical information even under test distributions. However, this strategy is not optimal due to two issues. Firstly, there is a significant distribution shift between the test and training sets, which may result in incorrect invariant subgraphs being extracted during the test phase. Secondly, the extraction of subgraphs can lead to the loss of important structural information. To address these issues, we propose a novel two-stage training paradigm named \textbf{I}nvariant \textbf{G}raph \textbf{T}est-\textbf{T}ime \textbf{T}raining (\textbf{IGT3}), which uses the training paradigm called Test-Time Training (TTT). We also propose invariant graph recognition to learn key parts of the graph without destroying the original graph structure. In addition, we introduce a multi-level graph contrastive learning method as an auxiliary self-supervised task. To prevent TTT failure, we use the feature constraint to enhance the robustness of the training process. We conduct experiments on seven common graph classification OOD benchmark datasets. The results demonstrate that IGT3 outperforms state-of-the-art (SOTA) methods by an average of 4.2\%, confirming the effectiveness of each module. The paper code is available at https://***/r/IGT3-3281/ © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Steganography for Neural Radiance Fields by Backdooring

arXiv

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arXiv 2023年

作者： Dong, Weina Liu, Jia Ke, Yan Chen, Lifeng Sun, Wenquan Pan, Xiaozhong The College of Cryptography Engineering Engineering University of PAP Shaanxi Xi’an710086 China Key Laboratory of Network and Information Security of PAP Engineering University of PAP Shaanxi Xi’an710086 China

The utilization of implicit representation for visual data (such as images, videos, and 3D models) has recently gained significant attention in computer vision research. In this letter, we propose a novel model steganography scheme with implicit neural representation. The message sender leverages Neural Radiance Fields (NeRF) and its viewpoint synthesis capabilities by introducing a viewpoint as a key. The NeRF model generates a secret viewpoint image, which serves as a backdoor. Subsequently, we train a message extractor using overfitting to establish a one-to-one mapping between the secret message and the secret viewpoint image. The sender delivers the trained NeRF model and the message extractor to the receiver over the open channel, and the receiver utilizes the key shared by both parties to obtain the rendered image in the secret view from the NeRF model, and then obtains the secret message through the message extractor. The inherent complexity of the viewpoint information prevents attackers from stealing the secret message accurately. Experimental results demonstrate that the message extractor trained in this letter achieves high-capacity steganography with fast performance, achieving a 100% accuracy in message extraction. Furthermore, the extensive viewpoint key space of NeRF ensures the security of the steganography scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Steganography

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

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Proposal Feature Learning Using Proposal Relations for Weakly Supervised Object Detection

Proposal Feature Learning Using Proposal Relations for Weakl...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhaofei Wang Weijia Zhang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle NSW Australia

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Weakly Supervised Object Detection (WSOD) trains detectors using only image-level annotations. Most existing WSOD models are based on pre-computed proposals and do not fully explore the relations of proposals. In this work, we address this limitation by proposing two approaches of Proposal Feature Learning for WSOD (PFL-WSOD), which effectively capture intra-proposal relations and inter-proposal relations respectively, thus improving proposal representation. To extract intra-proposal relations, we propose to utilize Self-Attention on Single Proposal for capturing relations inside each proposal. For inter-proposal relations, we propose Salient Region Banks by capturing a unique type of inter-proposal relation called deep inclusion, which significantly improves proposal representation when used in synergy with contrastive learning. Experimental results on benchmarks demonstrate the effectiveness of our methods.

关键词： Representation learning Annotations Object detection Contrastive learning Detectors Benchmark testing Feature extraction

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Trading Trust for Privacy: Socially-Motivated Personalized Privacy-Preserving Collaborative Learning in IoT

Trading Trust for Privacy: Socially-Motivated Personalized P...

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International Conference on computer Supported Cooperative Work in Design

作者： Yuliang Chen Xi Lin Gaolei Li Lixing Chen Jing Wang Siyi Liao Jianhua Li Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China China United Network Communication Co. Ltd

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Nowadays, collaborative federated learning (CFL) is developing rapidly in the Internet of Things (IoT), which allows clients to jointly train models without compromising private data. The existing research has studied alone either trust enhancement or privacy preservation issues in CFL. Due to the highly coupled nature of trust and privacy in a collaborative environment, it is worth investigating how to balance appropriate trust and privacy tradeoffs for realizing high-quality CFL. In this paper, we come up with the idea of "trading Trust for Privacy", and propose a novel socially-motivated personalized privacy-preserving federated learning (SP-PFL) framework, which aims to realize social trust-grained privacy protection. First, we design a social trust evaluation method among CFL clients, which is based on topological relation and attribute similarity. Based on the obtained trust value, we then propose a trust-grained privacy budget allocation strategy for SP-PFL, which could further adaptively adjust the differential privacy (DP) noise perturbation. Besides, we provide an analysis of privacy and convergence for our SP-PFL. Finally, we experiment with different models and parameter settings on different datasets. Extensive experimental results show that our method maintains personalized privacy and effectively improves the accuracy by 6.11% on the CNN model and MNIST dataset.

关键词： Privacy Differential privacy Federated learning Computational modeling Perturbation methods Noise Collaboration

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