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Data Leakage with Label Reconstruction in Distributed Learning Environments 1

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhang, Xiaoxue Zhou, Xiuhua Chen, Kongyang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Pazhou Lab Guangzhou China Jiangsu Key Laboratory of Media Design and Software Technology Jiangnan University Wuxi China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Distributed learning is commonly applied for the high demands of computation resources while training models with large-scale data. However, existing solutions revealed that it may lead to information leakage of private data. Attackers are able to reconstruct data via minimizing the difference between shared gradients and fake gradients that are produced by themselves. This is so-called data leakage attack from gradients. We find that, in many cases, such attack can only obtain random noises or extreme fuzzy information, which are quite different from the raw data. To deal with this problem, we exploit the gradient updates as well as their labels during the parameter-server training, and discover significant information losses during the label coding. Thus, in our method, we reconstruct the labels of samples with a fine-grained method, and we recover the raw inputs with these processed labels. We test our method on different datasets (i.e. CIFAR100 dataset and solid color images) and obtained the reconstructed results which are obviously clearer than the ones of the state-of-the-arts. The results prove the effectiveness of our algorithm in fixing defects to some extent as well. Extensive experiments are also conducted to explore defense strategies against data leakage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data privacy

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Augmented Feature Representation with Parallel Convolution for Cross-domain Facial Expression Recognition 16th

Augmented Feature Representation with Parallel Convolution ...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Yang, Fan Xie, Weicheng Zhong, Tao Hu, Jingyu Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (纸本)9783031202322

Facial expression recognition (FER) has made significant progress in the past decade, but the inconsistency of distribution between different datasets greatly limits the generalization performance of a learned model on unseen datasets. Recent works resort to aligning feature distributions between domains to improve the cross-domain recognition performance. However, current algorithms use one output each layer for the feature representation, which can not well represent the complex correlation among multi-scale features. To this end, this work proposes a parallel convolution to augment the representation ability of each layer, and introduces an orthogonal regularization to make each convolution represent independent semantic. With the assistance of a self-attention mechanism, the proposed algorithm can generate multiple combinations of multi-scale features to allow the network to better capture the correlation among the outputs of different layers. The proposed algorithm achieves state-of-the-art (SOTA) performances in terms of the average generalization performance on the task of cross-database (CD)-FER. Meanwhile, when AFED or RAF-DB is used for the training, and other four databases, i.e. JAFFE, SFEW, FER2013 and EXPW are used for testing, the proposed algorithm outperforms the baselines by the margins of 5.93% and 2.24% in terms of the average accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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AMSC: Adaptive Multi-channel Graph Convolutional Network-Enhanced Web Services Classification 23

AMSC: Adaptive Multi-channel Graph Convolutional Network-Enh...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Qing, Yueying Cao, Buqing Peng, Mi Zhang, Lulu Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the development of Service Oriented Architecture (SOA), the number of Web services on the Internet is also growing rapidly. Classifying Web services accurately and efficiently is helpful to improve the quality of services discovery and promote the efficiency of service composition. However, existing deep learning-based Web services classification methods, such as graph convolutional networks (GCNs), are incapable of adaptively learning the correlation between service topology structure and service node features concurrently, resulting in unsatisfactory classification performance. To address this problem, this paper proposes an adaptive multi-channel GCN-enhanced Web services classification method. In this method, we first extract specific and shared embedding, in the Web API node isomorphic network, from the node features, topology, and combination of Web service nodes. Then, an attention mechanism is used to learn the importance weight of each embedding. By doing this, we adaptively integrate these weights to ensure the consistency and difference of each learned embedding. Finally, experimental results on real datasets from Programmable Web show that compared with LINE, Node2vec, Deep-walk, GCN, and GAT, the method proposed in this paper has an average improvement in accuracy of 19.81%, 19.35%, 19.16%, 11.56%, and 7.75% respectively. © 2021 IEEE.

关键词： Correlation Adaptive systems Network topology Smart cities Quality of service Feature extraction Topology

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Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration

arXiv

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arXiv 2025年

作者： Chang, Laibin Wang, Yunke Du, Bo Xu, Chang The School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The School of Computer Science The University of Sydney Australia

Underwater imaging often suffers from significant visual degradation, which limits its suitability for subsequent applications. While recent underwater image enhancement (UIE) methods rely on the current advances in deep neural network architecture designs, there is still considerable room for improvement in terms of cross-scene robustness and computational efficiency. Diffusion models have shown great success in image generation, prompting us to consider their application to UIE tasks. However, directly applying them to UIE tasks will pose two challenges, i.e., high computational budget and color unbalanced perturbations. To tackle these issues, we propose DiffColor, a distribution-aware diffusion and cross-spectral refinement model for efficient UIE. Instead of diffusing in the raw pixel space, we transfer the image into the wavelet domain to obtain such low-frequency and high-frequency spectra, it inherently reduces the image spatial dimensions by half after each transformation. Unlike single-noise image restoration tasks, underwater imaging exhibits unbalanced channel distributions due to the selective absorption of light by water. To address this, we design the Global Color Correction (GCC) module to handle the diverse color shifts, thereby avoiding potential global degradation disturbances during the denoising process. For the sacrificed image details caused by underwater scattering, we further present the Cross-Spectral Detail Refinement (CSDR) to enhance the high-frequency details, which are integrated with the low-frequency signal as input conditions for guiding the diffusion. This way not only ensures the high-fidelity of sampled content but also compensates for the sacrificed details. Comprehensive experiments demonstrate the superior performance of DiffColor over state-of-the-art methods in both quantitative and qualitative evaluations. © 2025, CC BY.

关键词： Budget control

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Energy Conserved Failure Detection for NS-IoT Systems

arXiv

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arXiv 2024年

作者： Liu, Guojin Zhou, Jianhong Su, Hang Xiong, Biaohong Niu, Xianhua School of Computer and Software Engineering Xihua University China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China China

Nowadays, network slicing (NS) technology has gained widespread adoption within Internet of Things (IoT) systems to meet diverse customized requirements. In the NS based IoT systems, the detection of equipment failures necessitates comprehensive equipment monitoring, which leads to significant resource utilization, particularly within large-scale IoT ecosystems. Thus, the imperative task of reducing failure rates while optimizing monitoring costs has emerged. In this paper, we propose a monitor application function (MAF) based dynamic dormancy monitoring mechanism for the novel NS-IoT system, which is based on a network data analysis function (NWDAF) framework defined in Rel-17. Within the NS-IoT system, all nodes are organized into groups, and multiple MAFs are deployed to monitor each group of nodes. We also propose a dormancy monitor mechanism to mitigate the monitoring energy consumption by placing the MAFs, which is monitoring non-failure devices, in a dormant state. We propose a reinforcement learning based PPO algorithm to guide the dynamic dormancy of MAFs. Simulation results demonstrate that our dynamic dormancy strategy maximizes energy conservation, while proposed algorithm outperforms alternatives in terms of efficiency and stability. Copyright © 2024, The Authors. All rights reserved.

关键词： Internet of things

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DA-LGNet: Enhancing Spatial-Spectral feature representation with Dual-Attention Local-General Network for Hyperspectral images and Multispectral images Fusion

DA-LGNet: Enhancing Spatial-Spectral feature representation ...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Haozheng Zhang Yanhong Yang Zhixuan Jing Shengyong Chen School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Hyperspectral image and Multispectral image (HSI-MSI) fusion aims to fuse a registered high-resolution multi-spectral image (HR-MSI) with a low-resolution hyperspectral image (LR-HSI) to generate a spatially enhanced HSI with high spectral resolution. Current fusion methods often make insufficient utilization of spatial and spectral prior information, including spatial self-similarity and inter-spectral correlations, resulting in a degradation in image fidelity. Therefore, we propose a novel HSI-MSI fusion network, called DA-LGNet, designed to learn spatial-spectral priors with a dual-attention mechanism for feature enhancement and utilize the advantages of the large kernel attention for global and local feature representation. Specifically, the large kernel attention module (LKAM) can efficiently extract and integrate long-range dependencies and detailed textual information. The dual-attention enhancement module (DAEM) incorporates the position attention mechanism with the channel attention mechanism, flexibly capturing spatial and spectral prior information, which are crucial for restoring high-resolution HSI (HR-HSI). Extensive experiments on two datasets demonstrate that our DA-LGNet importantly outper-forms other state-of-the-art methods.

关键词： Attention mechanisms Convolution Fuses Feature extraction Transformers Image restoration Kernel

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High-Fidelity and High-Efficiency Talking Portrait Synthesis With Detail-Aware Neural Radiance Fields

引用

IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 PP-PP页

作者： Wang, Muyu Zhao, Sanyuan Dong, Xingping Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China Beijing Institute of Technology School of Computer Science Beijing China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China

In this paper, we propose a novel rendering framework based on neural radiance fields (NeRF) named HH-NeRF that can generate high-resolution audio-driven talking portrait videos with high fidelity and fast rendering. Specifically, our framework includes a detail-aware NeRF module and an efficient conditional super-resolution module. Firstly, a detail-aware NeRF is proposed to efficiently generate a high-fidelity low-resolution talking head, by using the encoded volume density estimation and audio-eye-aware color calculation. This module can capture natural eye blinks and high-frequency details, and maintain a similar rendering time as previous fast methods. Secondly, we present an efficient conditional super-resolution module on the dynamic scene to directly generate the high-resolution portrait with our low-resolution head. Incorporated with the prior information, such as depth map and audio features, our new proposed efficient conditional super resolution module can adopt a lightweight network to efficiently generate realistic and distinct high-resolution videos. Extensive experiments demonstrate that our method can generate more distinct and fidelity talking portraits on high resolution (900 × 900) videos compared to state-of-the-art methods. Our code is available at https://***/muyuWang/HHNeRF. © 1995-2012 IEEE.

关键词： Rendering (computer graphics)

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Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data

arXiv

引用

arXiv 2024年

作者： Zou, Xin Liu, Weiwei School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

Out-of-distribution (OOD) generalization has attracted increasing research attention in recent years, due to its promising experimental results in real-world applications. In this paper, we study the confidence set prediction problem in the OOD generalization setting. Split conformal prediction (SCP) is an efficient framework for handling the confidence set prediction problem. However, the validity of SCP requires the examples to be exchangeable, which is violated in the OOD setting. Empirically, we show that trivially applying SCP results in a failure to maintain the marginal coverage when the unseen target domain is different from the source domain. To address this issue, we develop a method for forming confident prediction sets in the OOD setting and theoretically prove the validity of our method. Finally, we conduct experiments on simulated data to empirically verify the correctness of our theory and the validity of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs

RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Jincheng Lu Miao Cai Baoliu Ye College of Computer Science and Software Engineering Hohai University Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

High fan-out requests are prevalent in systems employing multi-tier architectures. These requests are divided into several sub-requests for parallel processing. However, a high fan-out request must await all sub-requests to be completed before returning, but the processing times of sub-requests are unpredictable due to their differences in characteristics, such as data volume and data popularity. Meanwhile, existing SSD-based caches struggle to adjust request processing speeds to ensure timely handling. As a result, some sub-requests are delayed, affecting the overall latency and causing long tail latency *** paper proposes RSCache, a tail latency-friendly cache based on NVMe SSDs. RSCache combines the NVMe Weighted Round-robin (WRR) arbitration mechanism with a priority-based scheduling mechanism to enable differentiated request processing. We propose a fan-out size-based priority assignment strategy along with a latency-aware sub-request sorting method. They collaborate to prioritize sub-requests according to their impacts on tail latency and schedule them to NVMe priority queues with various processing speeds, effectively reducing the processing time variations among sub-requests. In addition, we balance the load across queues to avoid congestion with a novel feedback mechanism. We implement the prototype of RSCache based on SPDK. Our experiments demonstrate that RSCache reduces tail latency by up to 40% and improves throughput by two times compared to state-of-the-art SSD-based cache designs.

关键词： Schedules Distributed processing Prototypes Tail Parallel processing Throughput Sorting

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FakeDiffer: Distributional Disparity Learning on Differentiated Reconstruction for Face Forgery Detection 39

FakeDiffer: Distributional Disparity Learning on Differentia...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Bo Zhang, Zhao Zhao, Suiyi Ye, Xianming Zhang, Haijun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunnan China Department of Electronic and Computer Engineering University of Pretoria South Africa Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (纸本)157735897X

Existing face forgery detection methods achieve promising performance when training and testing forgery data are from identical manipulation types, while they fail to generalize well to unseen samples. In this paper, we experimentally investigate and find that the poor generalization of the methods mainly arises from their overfitting on the known fake patterns. Excessively focused on seen fakes, those detectors fail to effectively learn image-intrinsic information and the distributional disparity between real and fake images. Then, to address this issue, we redefine fake learning as real-fake distributional disparity learning. We propose a novel deepfake detection framework learning distributional disparity based on the differentiated reconstruction on real and fake images for improved generalization. Specifically, distributional disparity learning on differentiated reconstruction of the real and fake images, enforces the model to learn image-invariant intrinsic representations. The reconstruction on real and fake images forces the decoders to learn the distribution of real and fake images, respectively. Moreover, to avoid the influence from the specificalization of the known fake patterns, we further propose the information interaction learning on the encoded intrinsic information and the pixel disparity between the input image and its reconstruction to distinguish face forgeries that are even unknown. Extensive experiments on large-scale benchmark datasets demonstrated the effectiveness of addressing the overfitting issue of the classification network, and verified the superior performance of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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