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Generative Gradient Inversion via Over-Parameterized networks in Federated Learning

Generative Gradient Inversion via Over-Parameterized Network...

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ieee/CVF International conference on computer Vision (ICCV)

作者： Zhang, Chi Zhang, Xiaoman Sotthiwat, Ekanut Xu, Yanyu Liu, Ping Zhen, Liangli Liu, Yong ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9798350307184

Federated learning has gained recognitions as a secure approach for safeguarding local private data in collaborative learning. But the advent of gradient inversion research has posed significant challenges to this premise by enabling a third-party to recover groundtruth images via gradients. While prior research has predominantly focused on low-resolution images and small batch sizes, this study highlights the feasibility of reconstructing complex images with high resolutions and large batch sizes. The success of the proposed method is contingent on constructing an over-parameterized convolutional network, so that images are generated before fitting to the gradient matching requirement. Practical experiments demonstrate that the proposed algorithm achieves high-fidelity image recovery, surpassing state-of- the-art competitors that commonly fail in more intricate scenarios. Consequently, our study shows that local participants in a federated learning system are vulnerable to potential data leakage issues. Source code is available at https://***/czhang024/CI-Net.

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Spatial-Spectral Transformer for local and Global Hyperspectral Image Classification 2

Spatial-Spectral Transformer for Local and Global Hyperspect...

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2nd ieee International conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Cao, MengXin Zhao, Guixin Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798331515669

Deep learning techniques have become increasingly prevalent in hyperspectral image (HSI) classification. However, convolutional neural network (CNN)-based approaches often struggle with inefficient utilization of spectral sequence data and inadequate processing of fine-grained spectral details. In contrast, transformer models are inherently designed to capture sequence dependencies across spectral bands. Yet, most existing approaches tend to focus on either local or global feature extraction separately, which limits their ability to fully exploit the spectral information. To overcome these limitations, we propose the local and Global Fusion Spectral Transformer (LGFST), an innovative architecture that combines Swin Transformer blocks, standard Transformer blocks, and a novel local-Global Fusion (LGF) module. This architecture is designed to simultaneously capture both local and global features, thereby improving the extraction of detailed spectral information. The Swin Transformer block, integrated with patch merging and hierarchical attention mechanisms, expands the receptive field and enhances local feature representation through both intra- and cross-window interactions. Meanwhile, the Transformer block performs self-attention across all tokens to extract global features. The fusion of local and global features from these two blocks effectively improves the texture and discriminative power of spectral data, leading to enhanced classification performance, especially in scenarios with dense class distributions. Finally, the LGF module facilitates interaction between local and global feature sets, ensuring a seamless integration of information from both blocks. Extensive experiments on three benchmark HSI datasets demonstrate that LGFST outperforms conventional transformer models and sets new benchmarks in performance compared to state-of-the-art methods. © 2024 ieee.

关键词： Convolutional neural networks

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Enhancing Network Anomaly Detection Using Graph Neural networks 22

Enhancing Network Anomaly Detection Using Graph Neural Netwo...

引用

22nd Mediterranean Communication and computer Networking conference (MedComNet)

作者： Marfo, William Tosh, Deepak K. Moore, Shirley V. Univ Texas El Paso Dept Comp Sci El Paso TX 79968 USA

ISBN: (纸本)9798350390476;9798350390483

In the world of Internet of Things (IoT) networks, where devices are constantly communicating, keeping them secure from cyber threats is critical. This paper introduces a novel approach to detecting unusual and potentially harmful activities in these networks using graph neural networks (GNNs). We combine two specific types of GNNs-GraphSAGE and graph attention networks (GAT)-to create a model that understands and represents the behaviors and interactions in a network. GraphSAGE creates an embedding of network activities by examining local data interactions, while GAT directs the model's focus to the most critical interactions. By integrating these two methods in a single model that considers different types of interactions (both host and flow nodes), we aim to create a system that accurately represents the current state of a network and can also spot anomalies effectively while reducing false positives and negatives. Our innovative approach has demonstrated promising results, achieving an accuracy of 98% on the UNSW-NB15 dataset, significantly outperforming standalone GraphSAGE and GAT models. This underscores its potential as a robust framework for securing IoT networks against cyber threats and anomalies.

关键词： Graph neural networks deep learning networks anomaly detection security attacks

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Detecting Images Generated by Deep Diffusion Models using their local Intrinsic Dimensionality

Detecting Images Generated by Deep Diffusion Models using th...

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ieee/CVF International conference on computer Vision (ICCV)

作者： Lorenz, Peter Durall, Ricard L. Keuper, Janis Fraunhofer ITWM Kaiserslautern Germany Offenburg Univ IMLA Offenburg Germany

ISBN: (纸本)9798350307443

Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification. Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes. The code for our experiments is provided at https://***/deepfake-study/deepfake-multiLID.

关键词： deepfake detection diffusion models synthetic data

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SEPAL: Spatial Gene Expression Prediction from local Graphs

SEPAL: Spatial Gene Expression Prediction from Local Graphs

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ieee/CVF International conference on computer Vision (ICCV)

作者： Mejia, Gabriel Cardenas, Paula Ruiz, Daniela Castillo, Angela Arbelaez, Pablo Univ los Andes Ctr Res & Format Artificial Intelligence Bogota Colombia

ISBN: (纸本)9798350307443

Spatial transcriptomics is an emerging technology that aligns histopathology images with spatially resolved gene expression profiling. It holds the potential for understanding many diseases but faces significant bottlenecks such as specialized equipment and domain expertise. In this work, we present SEPAL, a new model for predicting genetic profiles from visual tissue appearance. Our method exploits the biological biases of the problem by directly supervising relative differences with respect to mean expression, and leverages local visual context at every coordinate to make predictions using a graph neural network. This approach closes the gap between complete locality and complete globality in current methods. In addition, we propose a novel benchmark that aims to better define the task by following current best practices in transcriptomics and restricting the prediction variables to only those with clear spatial patterns. Our extensive evaluation in two different human breast cancer datasets indicates that SEPAL outperforms previous state-of-the-art methods and other mechanisms of including spatial context.

关键词： Gene expression Graph neural networks Histopathology Spatial transcriptomics

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AlcFier: Adaptive Self-Learning Classifier for Routing in Vehicular Ad-Hoc Network 47

AlcFier: Adaptive Self-Learning Classifier for Routing in Ve...

引用

47th ieee conference on local computer networks (LCN)

作者： Nahar, Ankur Sikarwar, Himani Das, Debasis Indian Inst Technol Comp Sci & Engn Jodhpur Rajasthan India

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

This paper presents an adaptive self-learning classifier-based clustering algorithm called AlcFier, to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate mobility and channel characteristics (i.e., orientation, adjacency, link availability, queue occupancy, and signal-to-noise ratio) into the clustering approach as a channel-aware metric to provide a new direction to the taxonomy of the approaches employed to handle cluster head election, cluster affiliation, and cluster administration challenges. Experimental results show that AlcFier performs efficiently, improves cluster stability, reduces transmission delays, and improves throughput compared with the state-of-the-art routing protocols.

关键词： Vehicular ad hoc networks Clustering routing

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EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera 23

EventPoint: Self-Supervised Interest Point Detection and Des...

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23rd ieee/CVF Winter conference on Applications of computer Vision (WACV)

作者： Huang, Ze Sun, Li Zhao, Cheng Li, Song Su, Songzhi Xiamen Univ Xiamen Peoples R China Univ Sheffield Sheffield England Bosch Res North Amer Detroit MI USA

ISBN: (纸本)9781665493468

This paper proposes a self-supervised learned local detector and descriptor, called EventPoint, for event stream/camera tracking and registration. Event-based cameras have grown in popularity because of their biological inspiration and low power consumption. Despite this, applying local features directly to the event stream is difficult due to its peculiar data structure. We propose a new time-surface-like event stream representation method called Ten-code. The event stream data processed by Tencode can obtain the pixel-level positioning of interest points while also simultaneously extracting descriptors through a neural network. Instead of using costly and unreliable manual annotation, our network leverages the prior knowledge of local feature extraction on color images and conducts self-supervised learning via homographic and spatio-temporal adaptation. To the best of our knowledge, our proposed method is the first research on event-based local features learning using a deep neural network. We provide comprehensive experiments of feature point detection and matching, and three public datasets are used for evaluation (i.e. DSEC, N-Caltech101, and HVGA ATIS Corner Dataset). The experimental findings demonstrate that our method outperforms SOTA in terms of feature point detection and description.

关键词： Knowledge engineering Representation learning Power demand Annotations Neural networks Detectors Self-supervised learning

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CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation

CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Inter...

引用

ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Chowdhury, Townim Faisal Liao, Kewen Vu Minh Hieu Phan To, Minh-Son Xie, Yutong Hung, Kevin Rose, David van den Hengel, Anton Verjans, Johan W. Liao, Zhibin Univ Adelaide Australian Inst Machine Learning Adelaide SA Australia Australian Catholic Univ Fitzroy Vic Australia Flinders Univ S Australia Adelaide SA Australia Cent Adelaide Local Hlth Network SA Pathol Adelaide SA Australia

ISBN: (纸本)9798350353006

Deep Neural networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways to visually explain the DNN decision-making process by displaying 'attention' heatmaps of the DNNs. Nevertheless, the CAM explanation only offers relative attention information, that is, on an attention heatmap, we can interpret which image region is more or less important than the others. However, these regions cannot be meaningfully compared across classes, and the contribution of each region to the model's class prediction is not revealed. To address these challenges that ultimately lead to better DNN Interpretation, in this paper, we propose CAPE, a novel reformulation of CAM that provides a unified and probabilistically meaningful assessment of the contributions of image regions. We quantitatively and qualitatively compare CAPE with state-of-the-art CAM methods on CUB and ImageNet benchmark datasets to demonstrate enhanced interpretability. We also test on a cytology imaging dataset depicting a challenging Chronic Myelomono-cytic Leukemia (CMML) diagnosis problem. Code is available at: https://***/AIML-MED/CAPE.

关键词： class activation maps deep neural network interpretability explainable AI image recognition visual classification

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Image denoising based on Swin Transformer Residual Conv U-Net 27

Image denoising based on Swin Transformer Residual Conv U-Ne...

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27th ieee/ACIS International conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Gan, Yong Zhou, Shaohui Chen, Haonan Wang, Yuefeng Zhengzhou Univ Technol Zhengzhou Peoples R China Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou Peoples R China

ISBN: (纸本)9798350391961;9798350391954

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at sigma = 15, 33.87 and 0.915 at sigma = 25, and 28.96 and 0.810 at sigma = 50.

关键词： Image Denoising Deep Learning Transformer Convolutional Neural networks U-Net

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Enhancing Siamese Neural Network Performance with Custom local Binary Patterns for Face Matching 8

Enhancing Siamese Neural Network Performance with Custom Loc...

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8th ieee International conference on Information and Communication Technology, CICT 2024

作者： Koranga, Pawan Singh Indian Institute of Technology Kharagpur Dept. of Computer Science and Engineering Kharagpur India School of Computer Sciences Upes Dehradun248001 India

ISBN: (纸本)9798331531539

In this study, we investigate the efficacy of incorporating custom local binary patterns (LBP) as a preprocessing technique to enhance the performance of Siamese neural networks for face matching tasks. Our research focuses on training Siamese models using the DC Face dataset and comparing their performance when trained with and without LBP integration. The custom LBP function is designed to capture fine-grained texture details from facial images, providing robust feature representations that are invariant to variations in lighting conditions and facial expressions. Through rigorous experimentation and evaluation, we analyze the impact of LBP on the threshold point for image differentiation and compare the matching accuracy of Siamese models trained with and without LBP. Our findings reveal significant improvements in matching accuracy and robustness when utilizing the texture features extracted by the custom LBP function. By encoding local pixel structures and capturing subtle facial characteristics, LBP-enhanced Siamese models demonstrate enhanced performance in accurately matching faces across different images. Further-more, our study highlights the importance of using domain-specific preprocessing techniques, such as LBP, to optimize the capabilities of Siamese neural networks for face matching tasks. The results underscore the effectiveness of customizing feature extraction methods to suit the characteristics of the dataset, leading to improved matching accuracy and robustness. Overall, our research contributes valuable insights into the role of LBP in enhancing the performance of Siamese neural networks for image-based similarity tasks, with implications for various real-world applications in face recognition and biometric authentication. © 2024 ieee.

关键词： local binary pattern

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