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Shields for Digital Images: A Watermarking Method with KAN Block and Simulation-Enhanced Noise Pool to Resist Screen-Camera Attacks

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Guo, Daidou Qin, Chuan Luo, Xiangyang Feng, Guorui Zhang, Xinpeng University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai 200093 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450000 China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou 450001 China Shanghai University School of Communication and Information Engineering Shanghai 200444 China

To address the issues of privacy leakage and copyright infringement in screen-camera scenarios, we propose a robust image watermarking method, which incorporates kolmogorov-arnold network (KAN) blocks and a simulation-enhanced noise pool to resist screen-camera noise attacks. Specifically, we first modify the traditional convolutional blocks for processing high-dimensional features in the U-Net-based encoder to KAN blocks. This operation enhances the ability of encoder to model nonlinear relationships between complex features, while preserving global structure of the original image and minimizing the damage to local details caused by watermark embedding, thereby improving visual quality of the watermarked image. Additionally, to enhance the robustness of the proposed method against complex screen-camera noise attacks, a simulation-enhanced noise pool containing mathematical models and a deep noise simulation network, called NSim-Net, is designed. Especially, in NSim-Net, adversarial training between the simulator based on the improved U-Net and the discriminator based on PatchGAN effectively improves the ability to simulate complex noise. Experimental results demonstrate that, compared to typical screen-camera resilient watermarking methods, the watermarked image generated by the proposed method achieves a maximum peak signal-to-noise ratio (PSNR) improvement of 4.78 dB. Furthermore, based on our simulation-enhanced noise pool, the watermark extraction accuracy of the proposed method exceeds 98% under various screen-camera noise attacks. © 1991-2012 IEEE.

关键词： Imperceptibility Robustness Screen-Camera Resilient Watermarking Simulation-Enhanced Noise Pool

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Comparison of Missing Data Imputation Methods using the Framingham Heart study dataset

arXiv

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arXiv 2022年

作者： Psychogyios, Konstantinos Ilias, Loukas Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece

Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels and according to World Health Organization is the leading cause of death worldwide. EHR data regarding this case, as well as medical cases in general, contain missing values very frequently. The percentage of missingness may vary and is linked with instrument errors, manual data entry procedures, etc. Even though the missing rate is usually significant, in many cases the missing value imputation part is handled poorly either with case-deletion or with simple statistical approaches such as mode and median imputation. These methods are known to introduce significant bias, since they do not account for the relationships between the dataset’s variables. Within the medical framework, many datasets consist of lab tests or patient medical tests, where these relationships are present and strong. To address these limitations, in this paper we test and modify state-of-the-art missing value imputation methods based on Generative Adversarial Networks (GANs) and Autoencoders. The evaluation is accomplished for both the tasks of data imputation and post-imputation prediction. Regarding the imputation task, we achieve improvements of 0.20, 7.00% in normalised Root Mean Squared Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUROC) respectively. In terms of the post-imputation prediction task, our models outperform the standard approaches by 2.50% in F1-score. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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Label Correlation Guided Feature Selection for Multi-label Learning 9th

Label Correlation Guided Feature Selection for Multi-label ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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SlideMLP: A Pure Multi-layer Perceptrons Method For Medical Image Segmentation

SlideMLP: A Pure Multi-layer Perceptrons Method For Medical ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chaoqi Han Bingcai Chen Chanjuan Liu Qian Ning Victor C.M. Leung Shouzhen Jiao Qing Liu School of Computer Science and Technology Dalian University of Technology Dalian China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian China College of Electronics and Information Engineering Sichuan University Chengdu China Dept. of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Convolutional Neural Networks and Attention-based Transformer have emerged as the preferred models for medical image processing. Recently, specific network architectures relying solely on multilayer perceptrons (MLPs) have gained popularity and demonstrated excellent results in various computer vision tasks. In particular, CycleMLP has demonstrated good performance in dense prediction tasks owing to its adaptability to image size and linear computational complexity. However, the basic operator of CycleMLP has a fixed sampling location for any feature map and samples very few target organs in medical images characterized by an extreme imbalance between foreground and background. Therefore, effectively extracting the features of target organs becomes challenging. In this paper, we propose a new MLP-like module, SlideMLP, by considering the sparsity of target organs in medical images. This module extracts a set of offsets from the input feature maps and utilizes these offsets to re-select the sampling points. This approach effectively enhances the sampling rate of target organs while retaining the advantages of CycleMLP. Additionally, we constructed a U-shaped network with a pure MLP using this module and assessed its robustness using two datasets with different modalities. Comparative results with state-of-the-art methods demonstrate that the method proposed in this paper can achieve a substantial Dice Similarity Coe cient (DSC) while utilizing fewer parameters.

关键词： Image segmentation Neural networks Network architecture Multilayer perceptrons Feature extraction Transformers Robustness

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Soccer match broadcast video analysis method based on detection and tracking

Soccer match broadcast video analysis method based on detect...

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作者： Li, Hongyu Yang, Meng Yang, Chao Kang, Jianglang Suo, Xiang Meng, Weiliang Li, Zhen Mao, Lijuan Sheng, Bin Qi, Jun School of Information science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China School of Athletic Performance Shanghai University of Sport Shanghai China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong Univerity Shanghai China Department of Computing Xi'an JiaoTong-Liverpool University Suzhou China

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two-dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high-speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non-trivial. To mitigate this, we curate a large-scale, high-precision soccer ball detection dataset and devise a robust detection model, which achieved the (Formula presented.) of 80.9%. Additionally, we develop a high-speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real-time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations. © 2024 John Wiley & Sons Ltd.

关键词： Visualization

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Integrated Modeling and Optimal Operation of Multi-Energy System for Coastal Community

Integrated Modeling and Optimal Operation of Multi-Energy Sy...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Chen, Yang Chen, Jun Liu, Chenang Liu, Guodong Ferrari, Maximiliano Sundararajan, Aditya Department of Industrial and Systems Engineering North Carolina Agricultural and Technical State University Greensboro United States Department of Electrical and Computer Engineering Oakland University RochesterMI48309 United States School of Industrial Engineeering and Management Oklahoma State University Stillwater United States Electrification and Energy Infrastructures Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

ISBN: (纸本)9781665493765

Focusing on remote, isolated, and underserved communities, a multi-energy system is designed in this research which is capable of utilizing different energy sources in a more coordinated and energy-efficient way to support various demands, such as fresh water, electricity, hydrogen, thermal demand, etc. The energy sources considered are renewables (wind, solar, marine) and natural gas. The energy conversion process includes water desalination, gas combustion, water electrolyzation, and different types of storage (hydrogen tank, electricity, thermal, etc.) are designed to serve as buffers in supply-demand balancing. Sets of experiments are designed to demonstrate the effectiveness of the proposed operating model and investigate the impact of uncertainties from renewable generations and demands. © 2023 IEEE.

关键词： Desalination

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FEDERATED LEARNING WITH DYNAMIC CLIENT ARRIVAL AND DEPARTURE: CONVERGENCE AND RAPID ADAPTATION VIA INITIAL MODEL CONSTRUCTION

arXiv

引用

arXiv 2024年

作者： Chang, Zhan-Lun Han, Dong-Jun Parasnis, Rohit Hosseinalipour, Seyyedali Brinton, Christopher G. Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Computer Science and Engineering Yonsei University Seoul Korea Republic of The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY14260 United States

While most existing federated learning (FL) approaches assume a fixed set of clients in the system, in practice, clients can dynamically leave or join the system depending on their needs or interest in the specific task. This dynamic FL setting introduces several key challenges: (1) the objective function dynamically changes depending on the current set of clients, unlike traditional FL approaches that maintain a static optimization goal;(2) the current global model may not serve as the best initial point for the next FL rounds and could potentially lead to slow adaptation, given the possibility of clients leaving or joining the system. In this paper, we consider a dynamic optimization objective in FL that seeks the optimal model tailored to the currently active set of clients. Building on our probabilistic framework that provides direct insights into how the arrival and departure of different types of clients influence the shifts in optimal points, we establish an upper bound on the optimality gap, accounting for factors such as stochastic gradient noise, local training iterations, non-IIDness of data distribution, and deviations between optimal points caused by dynamic client pattern. We also propose an adaptive initial model construction strategy that employs weighted averaging guided by gradient similarity, prioritizing models trained on clients whose data characteristics align closely with the current one, thereby enhancing adaptability to the current clients. The proposed approach is validated on various datasets and FL algorithms, demonstrating robust performance across diverse client arrival and departure patterns, underscoring its effectiveness in dynamic FL environments. © 2024, CC BY.

关键词： Federated learning

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HSD-PAM: High Speed Super Resolution Deep Penetration Photoacoustic Microscopy Imaging Boosted by Dual Branch Fusion Network

arXiv

引用

arXiv 2023年

作者： Zhang, Zhengyuan Jin, Haoran Zheng, Zesheng Zhang, Wenwen Lu, Wenhao Qin, Feng Sharma, Arunima Pramanik, Manojit Zheng, Yuanjin The School of Electrical and Electronic Engineering Nanyang Technological University Singapore Zhejiang University College of Mechanical Engineering The State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou China Johns Hopkins University Electrical and Computer Engineering Baltimore United States Iowa State University Department of Electrical and Computer Engineering AmesIA United States

Photoacoustic microscopy (PAM) is a novel implementation of photoacoustic imaging (PAI) for visualizing the 3D bio-structure, which is realized by raster scanning of the tissue. However, as three involved critical imaging parameters, imaging speed, lateral resolution, and penetration depth have mutual effect to one the other. The improvement of one parameter results in the degradation of other two parameters, which constrains the overall performance of the PAM system. Here, we propose to break these limitations by hardware and software co-design. Starting with low lateral resolution, low sampling rate AR-PAM imaging which possesses the deep penetration capability, we aim to enhance the lateral resolution and up sampling the images, so that high speed, super resolution, and deep penetration for the PAM system (HSD-PAM) can be achieved. Data-driven based algorithm is a promising approach to solve this issue, thereby a dedicated novel dual branch fusion network is proposed, which includes a high resolution branch and a high speed branch. Since the availability of switchable AR-OR-PAM imaging system, the corresponding low resolution, undersample AR-PAM and high resolution, full sampled OR-PAM image pairs are utilized for training the network. Extensive simulation and in vivo experiments have been conducted to validate the trained model, enhancement results have proved the proposed algorithm achieved the best perceptual and quantitative image quality. As a result, the imaging speed is increased 16× times and the imaging lateral resolution is improved 5× times, while the deep penetration merit of AR-PAM modality is still reserved. © 2023, CC BY-NC-ND.

关键词： Photoacoustic microscopy

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

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A COMPREHENSIVE SURVEY ON APPLICATIONS OF TRANSFORMERS FOR DEEP LEARNING TASKS

arXiv

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arXiv 2023年

作者： Islam, Saidul Elmekki, Hanae Elsebai, Ahmed Bentahar, Jamal Drawel, Najat Rjoub, Gaith Pedrycz, Witold Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer Istanbul Turkey Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology. © 2023, CC BY-NC-ND.

关键词： Long short-term memory

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