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arXiv 2024年

作者： Li, Na Zhou, Chunyi Gao, Yansong Chen, Hui Fu, Anmin Zhang, Zhi Yu, Shui School of Cyberspace Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO CanberraACT2601 Australia Department of Computer Science and Software Engineering University of Western Australia PerthWA6009 Australia School of Computer Science University of Technology Sydney SydneyNSW2007 Australia

Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the 'right to be forgotten' of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia. While the area has developed rapidly, there is a lack of comprehensive surveys to capture the latest advancements. Recognizing this shortage, we conduct an extensive exploration to map the landscape of machine unlearning including the (fine-grained) taxonomy of unlearning algorithms under centralized and distributed settings, debate on approximate unlearning, verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. The survey concludes by outlining potential directions for future research, hoping to serve as a guide for interested scholars. Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

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GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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PolarBEVU: Multi-Camera 3D Object Detection in Polar Bird's-Eye View via Unprojection

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Hou, Minghui Lyu, Chuanhao Wang, Gang Ma, Baorui Xu, Rongtao Hu, Jue Fan, Xiaopeng Jilin University College of Computer Science and Technology Changchun China Tsinghua University School of Software Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Harbin Institute of Technology National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin150080 China Harbin Institute of Technology Centre for Composite Materials and Structures Harbin150001 China Harbin Institute of Technology Faculty of Computing Harbin150001 China

3D object detection from a Bird's Eye View (BEV) has emerged as a novel perception paradigm for autonomous driving scenarios. While most current 3D object detection methods still rely on the conventional Cartesian coordinates, they fail to align with the non-aligned coordinate system inherent in image geometry. The Polar coordinates, on the other hand, better fit with the geometric shape corresponding to the perception of cameras. However, transforming between coordinate systems introduces distortions in the perception information, resulting in issues such as "Weak Adaptability to Heatmap Distribution"and "Offset in the Center Point of the Bounding Box."To address these challenges, this paper proposes a cutting-edge 3D object detection model named PolarBEVU, which leverages the bird's-eye view under the Polar coordinates along with multi-camera unprojection. The model introduces an innovative "Deformable Uniform Heatmap Distribution"method that adjusts heatmap computations based on box shapes, generating high-quality heatmaps and effectively resolving the issue of "Weak Adaptability to Heatmap Distribution."Moreover, the model incorporates the concept of "Dynamic High-risk Regression Region"to enhance the accuracy and robustness of the center point regression at the bounding box, thus mitigating the issue of "Offset in the Center Point of the Bounding Box."In extensive experiments on the nuScenes dataset, PolarBEVU achieves impressive results with 49.9% mAP and 57.4% NDS on the test set, surpassing other comparative approaches and reaching the state-of-the-art (SOTA) performance among methods utilizing Polar coordinates. This clearly demonstrates the efficacy and superiority of PolarBEVU. In addition, the model is successfully deployed on Nvidia Jetson AGX Orin, showcasing real-time inference speeds of 31.42ms. These findings affirm PolarBEVU's potential for practical applications. Code is available at https: //***/JLUrob/PolarBEVU. © 2025 IEEE.

关键词： Object tracking

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Adaptive Data Transport Mechanism for UAV Surveillance Missions in Lossy Environments

Adaptive Data Transport Mechanism for UAV Surveillance Missi...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Niloufar Mehrabi Sayed Pedram Haeri Boroujeni Jenna Hofseth Abolfazl Razi Long Cheng Manveen Kaur James Martin Rahul Amin School of Computing Clemson University Clemson SC USA Computer Science Department California State University Los Angeles CA USA Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Unmanned Aerial Vehicles (UAVs) play an increasingly critical role in Intelligence, Surveillance, and Reconnaissance (ISR) missions such as border patrolling and criminal detection due to their ability to access remote areas and transmit real-time imagery to servers. However, UAVs face limitations in payload, power, and communication bandwidth, necessitating selective data transmission strategies. While traditional methods strive to preserve maximal information in transferred video frames, missing the fact that only certain parts of images/video frames are relevant for Object Detection and Tracking (OD/OT) in ISR missions. This paper adopts a different perspective and offers an alternative AI-driven scheduling policy that prioritizes selecting regions of the image that significantly contribute to the mission objective. The key idea is tiling the image into small patches and developing a Deep Reinforcement Learning (DRL) framework that assigns higher transmission probabilities to patches that present higher overlaps with the detected object of interest while penalizing sharp transitions over consecutive frames to promote smooth scheduling shifts. Although we used YOLOv8 object detection and UDP transmission protocols as a benchmark testing scenario, the idea is general and applicable to different transmission protocols and OD/OT methods. To further boost the system's performance and avoid OD errors for cluttered image patches, we integrate it with inter-frame interpolations. With this method, we achieved about 45% improvement in terms of OD accuracy for the proposed method (F1 score:98%) compared to random selection (F1 score: 53%) when the transmission budget is 50% (we afford sending half of the image patches). Under an extremely constrained transmission budget (5%), this gain can be as high as 87%. The only cost for such improvement is a feedback channel from the ground server to drones.

关键词： Interpolation Protocols Accuracy Surveillance Object detection Streaming media Autonomous aerial vehicles Deep reinforcement learning Real-time systems Servers

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Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

arXiv

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arXiv 2024年

作者： Wang, Zihan Song, Siyang Luo, Cheng Deng, Songhe Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China University of Leicester United Kingdom Monash University Australia

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatio-temporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and magnitude in different AUs’ activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR. © 2024, CC BY.

关键词： Face recognition

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Time series generator adversarial network with stochastic process for the degradation generation and prediction

Time series generator adversarial network with stochastic pr...

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Data Driven Control and Learning systems (DDCLS)

作者： Anqi Shangguan Nan Feng Lingxia Mu Rong Fei Xinhong Hei Guo Xie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of computer science and Engineering Xi'an University of Technology Xi'an China

Since the cumbersome collection process and high cost, the collected degradation of the product is basically small samples, which will affect the accuracy of reliability evaluation. It is necessary to expand the degradation to improve the accuracy of later reliability assessment. Therefore, a degradation generation and prediction method is proposed combining the time series generator adversarial network (TimeGAN) and stochastic process. Firstly, the input degradation is expanded by the sliding window to improve the later training accuracy; Then, the construction of the generator in TimeGAN is linked with the stochastic process to make the generation data more realistic. Finally, the results of degradation prediction by the Gated Recurrent Unit (GRU) can be obtained. Two datasets and different generation methods are adopted to evaluate the effectiveness of the proposed method. The results shows that the Kullback-Leibler(KL) divergence is the smallest, and the prediction error is the smallest compared with the other methods. So, the proposed method is proved that it is valid in the degradation generation and prediction, and can be used for the further reliability assessment of the product in the industrial system.

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Localization Method of Underwater Robot Based on Sonar Image 5

Localization Method of Underwater Robot Based on Sonar Image

引用

5th International Conference on Information Communication and Signal Processing, ICICSP 2022

作者： Li, Lirong Mei, Bing Chen, Peng Yu, Liang Gong, Pengcheng Hubei University of Technology Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Wuhan China Hubei University of Technology Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment Wuhan China School of Mechanical Engineering Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai China School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781665485890

In this paper, we propose a positioning algorithm based on sonar images, which is mainly used for the positioning of underwater robots, so that the robots can obtain the position information in real time when operating underwater and avoid colliding with the underwater walls. First, the underwater space was detected with multibeam sonar, and the sonar images of the underwater space wall were found to have line segment characteristics;Composite denoising, threshold segmentation and Canny edge detection are applied to the sonar image to extract the contours of the underwater spatial wall. Then the characteristic line segments of the underwater spatial wall are detected based on the LSD (Line Segment Detector) line segment detection algorithm. In terms of line segment classification, a method is proposed to effectively classify line segments using the origin of the sonar image and the slope of the detected line segment. To further demonstrate the effectiveness of the localization algorithm in this paper, the specific steps of the algorithm are illustrated with the example of underwater rectangular space. © 2022 IEEE.

关键词： Image segmentation

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PancakeFS: A Write Efficiently and Read Optimized Filesystem

PancakeFS: A Write Efficiently and Read Optimized Filesystem

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Yongkang Wang Yang Liu Zhiguang Chen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of High-Performance Computing Institute for Quantum Information College of Computer National University of Defense Technology Changsha China

With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Addressing the limitations of traditional file systems, we propose PancakeFS, an LSM-Tree-based file system. Leveraging the write-friendly characteristics of LSM-Tree, PancakeFS exhibits efficient write performance. We improve read performance through the design of an inode allocation algorithm based on subtree partitioning and optimization of the disk layout for hot data reconstruction. Our experimental results demonstrate that PancakeFS outperforms Ext4 and TableFS in terms of both read and write performance.

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Towards Scalable and Privacy-Preserving Data Sharing in Internet of Digital Twins

Towards Scalable and Privacy-Preserving Data Sharing in Inte...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xiangyu Shi Guanjie Li Tom H. Luan Zhou Su Shui Yu Wen Wu School of Cyber Engineering Xidian University Xi’an China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China School of Computer Science University of Technology Sydney Sydney Australia Frontier Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

A Digital Twin (DT) is a software agent of a physical entity in virtual space, transcending the limitations imposed by physical constraints to enable intelligent services. The emergence of the Internet of Digital Twins (IoDT), by connecting DTs as a network, provides a reliable solution for streamlining the sharing of extensive real-time data among DTs. However, ensuring the security and efficiency of highly sensitive DT data remains a significant challenge in the distributed and dynamic IoDT storage environment. In this paper, we propose a comprehensive scheme that integrates blockchain, Distributed Hash Table (DHT), and Attribute-Based Encryption (ABE) to establish an IoDT data-sharing system. First, we introduce an ABE scheme to achieve data confidentiality and fine-grained access control. By implementing a key generation protocol, we address key escrow concerns in ABE. Second, to ensure bidirectional data confidentiality in data sharing without revealing the identities of DTs, the DHT, and blockchain are jointly applied to achieve resource registration and discovery. Through an XOR mapping operation mechanism, DT data are distributed across different DHT network nodes, reducing DT nodes’ storage consumption and effectively addressing the scalability issue of IoDT storage. Finally, we provide performance evaluations to demonstrate the reliability and efficiency of our proposed scheme. Our scheme strikes a balance between efficiency and security, exhibiting efficient performance in IoDT data sharing compared to other ABE and blockchain schemes.

关键词： Access control Data privacy Scalability Software agents Real-time systems Blockchains Digital twins Internet Encryption Software reliability

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing system 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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