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Individual Property Inference Over Collaborative Learning in Deep Feature Space

Individual Property Inference Over Collaborative Learning in...

引用

2022 ieee international conference on Multimedia and Expo, ICME 2022

作者： Yang, Haoxin Wang, Yi Li, Bin Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen China Dongguan University of Technology Dongguan China Shenzhen Institute of Articial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Collaborative learning is used in multi-media applications to distribute computing tasks and data storage over multiple sites. Recent studies found that private data information can be derived from model updates between the server and clients. Yet, previous methods are limited by their capabilities of privacy inference in more general and practical situations. In this paper, we propose a novel property inference method in the deep feature space to overcome those limitations. In particular, our method can make inference decisions on the level of individual examples instead of a batch of examples. We can simultaneously perform multiple property inference attacks without the need of image reconstruction. The proposed method is evaluated on several image benchmark datasets, which demonstrates significant improvement of inference accuracy even in the presence of privacy protection schemes. © 2022 ieee.

关键词： image reconstruction

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UNSUPERVISED MULTI-TASK LEARNING FOR 3D SUBTOMOGRAM image ALIGNMENT, CLUSTERING AND SEGMENTATION 29

UNSUPERVISED MULTI-TASK LEARNING FOR 3D SUBTOMOGRAM IMAGE AL...

引用

ieee international conference on image processing (ICIP)

作者： Zhu, Haoyi Wang, Chuting Wang, Yuanxin Fan, Zhaoxin Uddin, Mostofa Rafid Gao, Xin Zhang, Jing Zeng, Xiangrui Xu, Min Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China King Abdullah Univ Sci & Technol Struct & Funct Bioinformat Grp Thuwal Saudi Arabia Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA USA Univ Calif Irvine Dept Comp Sci Irvine CA USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

3D subtomogram image alignment, clustering, and segmentation are vital to macromolecular structure recognition in cryo-electron tomography (cryo-ET). However, acquiring ground-truth labels to train a unified deep learning model that can simultaneously deal with these tasks is unaffordable. To this end, we propose an end-to-end unified multi-task learning framework to simultaneously complete the three tasks, where models are trained in an unsupervised manner without using any labels. In particular, we have three parallel branches. In the alignment branch, we adopt a two-stage training scheme, i.e., self-supervised pretraining and constrained unsupervised training using our proposed skip correlation attention layer and constrained loss. Synchronously, in the clustering branch, the learned deep cluster features are utilized to iteratively cluster subtomograms into groups using pseudo-labels from an image-wise Gaussian Mixture Model (GMM). Meanwhile, in the segmentation branch, we use rough pseudo-labels generated from a voxel-wise GMM as supervision signals, and prior knowledge from humans is utilized to jointly learn how to correct these labels as well as predict reliable segmentation results. Benefiting from the end-to-end unified network architecture, our method achieves overall state-of-the-art performance on both simulated and real subtomogram processing benchmarks.

关键词： unsupervised multi-task learning subtomogram alignment subtomogram cluster subtomogram segmentation

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Low-power-consumption coherent receiver architecture for satellite optical links

Low-power-consumption coherent receiver architecture for sat...

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ieee international conference on Space Optical Systems and applications (ICSOS)

作者： Bernini, A. W. Fice, M. J. Balakier, K. Univ Coll London UCL Dept Elect & Elect Engn London England

ISBN: (纸本)9781665434393

As the demand for satellite data transmission increases, higher capacity optical links need to be developed to allow satellites to be connected directly to ground stations (GST). The advantages of Low Earth Orbit (LEO) direct-to-Earth links are smaller latency when compared to relay systems using Geostationary Orbit (GEO) satellites, i.e. LEO-to-GEO and GEO-to-GST, and an increased available bandwidth offered by the optical spectrum with respect to radio frequency (RF) which allows for much higher link capacity. The increase in data rate of optical satellite to ground links towards 100 Gbps will require implementing optical coherent transceivers with capability to compensate for Doppler shift and atmospheric channel impairments. An important figure of merit which needs to be carefully considered in a satellite system is the equipment power consumption. The power consumption of coherent receivers used for terrestrial applications is closely related to the bit rate, with a receiver back-end digital signal processing being responsible for the vast majority of the power consumed. In this paper we propose a hybrid approach to signal processing consisting of simplified digital and analogue elements allowing for significant power reduction. Moreover, one of the attractive aspects of the proposed approach is that it does not require an increased complexity for an increase in baud rate. It will be discussed that the analogue approach to the frequency and phase recovery would allow a saving of approximately 40% to 50% of power on the overall DSP block at baud rates between 10 Gbaud and 100 Gbaud.

关键词： digital signal processing optical injection phase-lock loop (OIPLL) power consumption low-earth orbit (LEO) satellites coherent detection

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image Search Engine with Recognition 4

Image Search Engine with Recognition

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kumar, Jmsv Ravi Dheeraj, Bellaganti Gowtham, Geddada Babu, Dokala Vivek Kiran, Bonthu Asha Srikanth, M. Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331523923

The image Recognition Search Engine is one of the modern advances in artificial intelligence and image analysis and processing. This advanced system surpasses typical functionalities in image identification as it captures features to solve new problems faced in existing systems of digital media authentication, content designing, and improvement of images. The system core relies on advanced AI algorithms that can rasterically recognize and analyze various forms of image objects ranging from objects, faces, scenes, and brand logos converting what appears to be simple images into indexical, analyzable data. One feature is that the system can integrate deepfake detection technology, which is relevant in the context of increasing the amount of manipulated content in the media space. Using special algorithms to learn even the least detectable abnormality in images including the presence of unnatural distinctive facial characteristics and changes in lighting conditions, we obtain a highly effective tool for improving the reliability of image identification. Furthermore, the platform allows for new features to convert an image into a video by implementing frame interpolation and motion estimation through animating the images to allow users to create interesting clips for applications regarding marketing or any personal use. © 2025 ieee.

关键词： Motion estimation

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A 0.8V Intelligent Vision Sensor with Tiny Convolutional Neural Network and Programmable Weights Using Mixed-Mode processing-in-Sensor Technique for image Classification

A 0.8V Intelligent Vision Sensor with Tiny Convolutional Neu...

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2022 ieee international Solid-State Circuits conference, ISSCC 2022

作者： Hsu, Tzu-Hsiang Chen, Guan-Cheng Chen, Yi-Ren Lo, Chung-Chuan Liu, Ren-Shuo Chang, Meng-Fan Tang, Kea-Tiong Hsieh, Chih-Cheng National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781665428002

Vision systems with artificial intelligence (AI) for applications requiring image classification are in growing demand. However, the imager plus dedicated AI accelerator solution [1] suffers from the burdens of power and latency caused by the raw image data traffic between the imager and the companion signal processor with a neural network accelerator, making it unsuitable for the real-time inference in low-power edge devices. Recently, imagers with near- or in-sensor processing capability have been developed [2]-[6] to improve the system efficiency for specific applications. In [2]-[4], the near-sensor Haar-like filtering operations are implemented in imagers to realize face detection (FD). However, unlike using convolutional neural networks (CNNs) with programmable weights for different tasks, the implemented features of such prior works are limited and not configurable. In [5], a convolutional CMOS image sensor (CIS) with near-sensor analog multiply-accumulate (MAC) operations was reported for assisting with the 1st-layer computations of a CNN. However, the convolutional CIS is inadequate for some tasks, due limits on the numbers of layers/kernels, and needs a companion digital accelerator for the required operations (Rectified Linear Unit: ReLU, Maximum-Pooling: MP, Fully-Connected layer: FC, etc.) of a complete CNN model. In [6], an analog convolutional CIS is reported with a 5-layer network for CNN implementation. However, the analog MAC operations using charge sharing with a capacitor array leads to gain loss, low weight resolution, and limited accuracy. Moreover, the ReLU+MP operation using a static winner-take-all circuit is power hungry. To address these issues, we present an intelligent vision sensor (IVS) with an embedded tiny CNN model and programmable weights to achieve configurable feature extraction and on-chip image classification using a mixed-mode processing-in-sensor (PIS) technique. © 2022 ieee.

关键词： image classification

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A Haptic Stimulation-Based Training Method to Improve the Quality of Motor imagery EEG signal in VR 30

A Haptic Stimulation-Based Training Method to Improve the Qu...

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30th ieee conference Virtual Reality and 3D User Interfaces (ieee VR)

作者： Cheng, Shiwei Tian, Jieming Zhejiang Univ Technol Sch Comp Sci Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350348156

With the emergence of brain-computer interface (BCI) technology and virtual reality (VR), how to improve the quality of motor imagery (MI) electroencephalogram (EEG) signal has become a key issue for MI BCI applications under VR. In this paper, we proposed to enhance the quality of MI EEG signal by using haptic stimulation training. We designed a first-person perspective and a third-person perspective scene under VR, and the experimental results showed that the left- and right-hand MI EEG quality of the participants improved significantly compared with that before training, and the mean differentiation of the left- and right-hand MI tasks was improved by 21.8% and 15.7%, respectively. We implemented a BCI application system in VR and developed a game based on MI EEG for control of ball movement, in which the average classification accuracy by the participants after training in the first-person perspective reached 93.5%, which was a significant improvement over existing study.

关键词： Human-centered computing Human-computer interaction(HCI) Brain-computer interface Human-centered computing Human-computer interaction(HCI) Interaction paradigms Virtual reality Human-centered computing Human-computer interaction(HCI) Interaction devices Haptic stimulation

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Improving image-recognition Edge Caches with a Generative Adversarial Network

Improving Image-recognition Edge Caches with a Generative Ad...

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ieee international conference on Communications (ICC)

作者： Souza, Guilherme B. Pacheco, Roberto G. Couto, Rodrigo S. Univ Fed Rio de Janeiro GTA PEE COPPE DEL Poli Rio De Janeiro RJ Brazil

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

image recognition is an essential task in several mobile applications. For instance, a smartphone can process a landmark photo to gather more information about its location. If the device does not have enough computational resources available, it offloads the processing task to a cloud infrastructure. Although this approach solves resource shortages, it introduces a communication delay. image-recognition caches on the Internet's edge can mitigate this problem. These caches run on servers close to mobile devices and stores information about previously recognized images. If the server receives a request with a photo stored in its cache, it replies to the device, avoiding cloud offloading. The main challenge for this cache is to verify if the received image matches a stored one. Furthermore, for outdoor photos, it is difficult to compare them if one was taken in the daytime and the other at nighttime. In that case, the cache might wrongly infer that they refer to different places, offloading the processing to the cloud. This work shows that a well-known generative adversarial network, called ToDayGAN, can solve this problem by generating daytime images using nighttime ones. We can thus use this translation to populate a cache with synthetic photos that can help image matching. We show that our solution reduces cloud offloading and, therefore, the application's latency.

关键词： image edge detection image retrieval Generative adversarial networks User experience Mobile handsets Mobile applications Servers

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WSN based IoT applications: A Review 10

WSN based IoT applications: A Review

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10th international conference on Emerging Trends in Engineering and Technology - signal and Information processing, ICETET-SIP-22 2022

作者： Gupta, Anuradha Gulati, Tarun Bindal, Amit Kumar ECE Department Mullana India

ISBN: (纸本)9781665467414

Internet of Things (IoT) is an emerging technology that can be incorporated with wireless sensor networks (WSN) to extend its communication capabilities but the concept of WSN-IoT invites new challenges for the researchers as well as for the end users also. This paper investigates its applications for different domains (home/ industrial/ healthcare/ environment/ surveillance) and different issues associated with data acquisition /aggregation, optimal energy consumption and harvesting for smart devices and scalable communication etc. © 2022 ieee.

关键词： Wireless sensor networks Energy consumption Surveillance Data acquisition Information processing Market research Internet of Things

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CAMFNet: A Context-Aware Multiscale Fusion Network for Enhanced Multispectral image Fusion in Remote Sensing and Security 4

CAMFNet: A Context-Aware Multiscale Fusion Network for Enhan...

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4th international conference on Multimedia processing, Communication and Information Technology, MPCIT 2024

作者： Suneel, J.S. Dileep, M.R. Visvesvaraya Technological University Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375466

This research is mainly concerned with enhancing the object recognition and classification of multispectral imagery imagery especially of people and vehicles in the heavily vegetated regions. Sub-technology such as PCA and WT have inherent limit with regards to the ability to preserve both spatial and spectral properties. To fill the above-mentioned gaps, this work presents a CAMFNet;a framework that integrates multiscale fusion together with Visible (VIS), Near-Infrared (NIR) and Thermal Infrared (TIR) data for fusion and feature extraction and classification. CAMFNet uses classifiers like Gradient Tree Boost and SVM for differentiating people, cars, trees and other such objects. Feature extraction is carried out using PCA and HOG for enhancing the methods prior to the extraction. One of the radical innovations is the employment of certain aspects in CAMFNet, including multiscale feature extraction and attention mechanisms for boosted data fusion. The framework also involves generative aerial networks (GANs) and it is capable of preserving important spatial and spatial details profiles thereby making it to be more accurate, to more recalls and has low false positives compared to normal methods. Experiments made on real-world datasets coming from different landscapes, revealed that the CAMFNet outperformed the other networks in the classification metrics, as well as in PSNR and SSIM. Surveillance, agriculture, and the military are some of its uses to emphasize work in large-scale and distant investigations. It pushes the state-of-art in multispectral image fusion with this strong, scalable, real-time method for nonmilitary and military domains for large-scale objects recognition. © 2024 ieee.

关键词： image fusion

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image Matching Algorithm Under Adverse Conditions Based on SIFT 14

Image Matching Algorithm Under Adverse Conditions Based on S...

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14th international conference on Information Technology in Medicine and Education, ITME 2024

作者： Chen, Hao Cao, Xinrong Li, Zuoyong Lin, Lihui Fuzhou University College of Computer and Big Data Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Fujian354300 China

ISBN: (纸本)9798350356670

image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance. © 2024 ieee.

关键词： image matching

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